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新论文 · v1arXiv:2611.13704

SeqFold: Timestamp-Addressable Compressed States for User Action Sequence Backbones

Mianwei Zhou, Duo Zhang, Henry Tsai, Yuqi Zheng, Jian Li, Juexin Lin, Sujay Khandagale, Vijay Rajaram 等 10 位作者

原文摘要

Existing sequential user modeling methods mainly focus on improving the backbone encoder or learning task-specific user representations for a single prediction objective. We instead study how to export the multi-layer user states of a frozen long-history backbone as compact, timestamp-addressable representations for downstream training backfill and serving. In this setting, the user state at a given timestamp summarizes the user's actions before that time, so downstream systems need only the state aligned to each target timestamp rather than the full hidden-state timeline. To this end, we present \texttt{SeqFold}, a compressed-state interface that combines learned multi-channel fusion, channel-specific temporal decay, and Lookup-Free Quantization (LFQ) to produce compact discrete codes from frozen backbone states. Task-specific decoders then map these shared codes into flexible embedding spaces for downstream models. To make timestamp-addressable export practical at scale, we further develop a fused streaming implementation that updates the fusion recurrence after every action while materializing states only at target timestamps. On large-scale proprietary datasets, SeqFold reduces per-state representation storage by $3{,}400\times$ while preserving most of the downstream ranking performance of the uncompressed model, showing that SeqFold is an effective interface for reusing long-history sequence backbones in downstream models.

cs.AI · cs.SI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.15664

CIDERS: Cloud-Edge LLM Collaborative Learning via Accelerating Personalized Bilevel Optimization

Victor H. Chen, Hairui Yu, Stella K. Chung, Hong Yan

原文摘要

Amid the rapid advancement of physical-world intelligence, cloud-edge collaborative large language models (LLMs) have emerged as a promising roadmap for practical LLM deployment. However, existing cloud-edge paradigms struggle to balance global consensus with local personalization, which fails to satisfy the need for a unified knowledge foundation on the cloud and domain-specific adaptation at the edge. To address this, we introduce, for the first time, a personalized bilevel optimization framework that formalizes cloud-edge LLM collaboration as a dual structure: the upper level optimizes edge-side personalization, while the lower level governs cloud-side knowledge transfer, reaching cloud-edge evolving in coordination. We then propose CIDERS, an efficient solver that decomposes the model into a learnable backbone and a messenger. While the cloud performs knowledge transfer to the learnable backbone, the key lies in embedding global trajectories into each local personalization step via consensus-variate correction to reconcile personalization with consensus. We provide a comprehensive theoretical analysis, including a geometric characterization of the local trajectory and a full convergence guarantee, revealing an explicit trade-off structure between personalization and global convergence. Extensive experiments demonstrate that CIDERS consistently outperforms competitive baselines on the compressed edge path, with 3.1x and 1.7x gains on mathematical reasoning and code generation, respectively, and a 10\% relative gain on instruction metrics. Mechanism experiments attribute these gains to early consensus-corrected coordination and task-aware distillation. Overall, CIDERS offers a viable path toward consensus-guided continuous personalization in cloud-edge LLM systems.

cs.DC · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.00033

Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration

Xinghao Zhu, Zixi Liu, Shalin Jain, Chenran Li, Milad Noori, Michael Andres Lin, Huihua Zhao, John Welsh 等 21 位作者

原文摘要

Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains challenging. We present Contact Wrench Guidance from Human Demonstration in Robotic Dexterous Manipulation (CHORD), a framework for long-horizon manipulation of rigid and articulated objects with reinforcement learning. The key idea is object-centric contact wrench space guidance: we represent human and robot motions by the forces and torques they can induce on the object, enabling similarity to be measured by the induced instantaneous motions. This guidance makes reinforcement learning more scalable for contact-rich dexterous manipulation. We further introduce a large-scale simulation benchmark with 4,739 bimanual dexterous manipulation tasks, constructed from motion-capture datasets and reconstructed in-house videos. Evaluated on 1,831 benchmark tasks, CHORD achieves an average success rate of 82.12%, demonstrating strong scalability. CHORD also generalizes to whole-body manipulation from hand-only and third-person demonstrations, achieving a 90.77% success rate, and the learned policies transfer to the real world in both open-loop and closed-loop settings.

cs.RO · cs.AI · cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2611.05726

Do Language Models Know Which Constraints Belong Together?

Junbo Jacob Lian, Kaixuan Chen, Hanzhang Qin, Huiling Chen, Chaoyu Zhang, Chung-Piaw Teo

原文摘要

Dantzig--Wolfe reformulation strengthens the linear relaxation of a mixed-integer program by convexifying constraint families, and each candidate costs a column generation run. The choice is hard when families strengthen the bound only together, as in classical lot-sizing, network-design, and facility-location formulations. We prove that when an evaluation convexifies at most $s$ of $k$ families, methods that observe only bounds need of order $k^w$ evaluations to find a hidden group of $w$ families in the worst case. A prediction missing $e$ members reduces this to $Θ(k^e)$ through a staged search that need not know $e$. The orders extend to several groups, prices, and growing evaluation costs. We study two predictors: a lookahead at an optimal LP dual, which vanishes on groups whose families share no variable, and language models reading the model, verified by the staged search in JointDW. Such groups appear in three of ten MIPLIB-NL models whose bound can be improved and in all 59 benchmark instances. Given the coefficients of models with up to 16 families, one language model names a true group first in 249 of 252 answers. On the benchmark instances, decompositions chosen by language models from the text solve faster than those of GCG's detector.

cs.LG · cs.AI · cs.DS · math.OC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.21067

PrefReward: Learning User Preference Matrix for Personalized Text Generation

Yue Wu, Chengbing Wang, Yimeng Bai, Xiaoyan Zhao, Yang Zhang, Fuli Feng

原文摘要

Large Language Models (LLMs) have demonstrated remarkable ability in generating personalized content by leveraging user histories and contextual cues. However, most existing personalization approaches rely on implicit representations within model parameters, making it difficult to interpret user-specific preferences or effectively handle long-context dependencies. To address these challenges, we propose PrefReward, a novel preference-aware generative framework that explicitly models user styles through a structured preference matrix and integrates it into the decoding process as a reward signal. PrefReward consists of two stages: (1) extracting a user-specific preference matrix that summarizes individual stylistic tendencies, and (2) using the matrix to guide generation via a KL-divergence-based reward function. Experiments on the LongLaMP dataset show that PrefReward outperforms retrieval-based baselines and context steering method in both generation quality and personalization interpretability.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13694

Decision-Focused Learning via Neural Optimization Layers for Battery-Assisted Grid-Import Smoothing in AI Data Centers

Xinda Zheng, Canchen Jiang, Frits de Nijs, Edward Lam, Markus Wagner

原文摘要

Rapid AI data-center power fluctuations challenge grid integration. Sustained grid-import smoothing requires allocating limited battery power and energy under uncertain demand. We formulate risk-aware BESS scheduling that coordinates advance reserve commitments and demand-responsive dispatch, accounting for operating costs, grid-ramp penalties, and their conditional value-at-risk. We develop battery scheduling decision-focused learning (BS-DFL) with a constraint-aware neural optimization layer trained directly from this objective without optimal-solution labels. The trained layer is then frozen to guide demand-predictor learning through scheduling costs and ramping risk, enabling solver-free rolling-horizon scheduling. In a 10,000-minute simulation using loads derived from measured AI workloads, BS-DFL reduces mean absolute minute-to-minute grid-import changes by 71.70% and total scheduling cost by 18.01% relative to an MSE-trained predictor coupled with a mixed-integer linear programming solver, with an average decision time of 18.77 ms.

eess.SY · cs.LG · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13693

Rotating Walks, Derandomized Squares, and Permutation Conjugated Walks

Vedat Levi Alev, Shravas Rao

原文摘要

Using the recent strong convergence results of Bordenave and Collins (Annals of Mathematics) and Chen et al. (Annals of Mathematics), we study the average case behavior of rotating random walks of Cohen and Maor (STOC 2023) and the derandomized square construction of Rozenman and Vadhan (RANDOM 2005). Concretely, we prove a conjecture of Cohen et al. (ITCS 2025) regarding the average case behavior of derandomized squaring for random graphs and show that the existential bounds of Cohen and Maor for their rotating random walk construction hold with high-probability for random graphs. Using the results of O'Donnell and Wu (FOCS 2020), we can in polynomial time construct a family of graphs and rotations which almost saturate the bounds of Cohen and Maor, and meet the bounds conjectured by Cohen et al. for all vertex-transitive auxiliary graphs of fixed degree in the derandomized square. Finally, we study independent random permutation conjugates of deterministic regular graph adjacency matrices. We prove quantitative weak convergence to freely independent tree adjacency operators when the degrees are fixed and the girths tend to infinity. Separately, a covering argument gives deterministic spectral lower bounds for polynomials with non-negative coefficients, recovering the lower bound of Cohen et al. for rotating walks.

math.CO · cs.DM · math.PR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.28063

Unified Synthesis of Compositional Speech and Sound from Free-Form Text Prompts

Yuyue Wang, Xihua Wang, Xin Cheng, Yijing Chen, Ruihua Song

原文摘要

Generating unified audio that naturally integrates speech and sounds from free-form text prompts remains challenging. For example, the prompt ``A fast drum roll ends with a cymbal crash, after which a man shouts: `Come on!'.'' describes several events that should appear in order and form a coherent recording rather than independent tracks that lack coherent temporal and semantic interactions. Current methods broadly follow two paradigms: pipeline methods couple separate text-to-sound and text-to-speech models, while unified models rely on a single generator that still requires structured, multi-field prompt inputs. Both paradigms require upstream prompt decomposition or rewriting, limiting free-form flexibility and risking cascading errors. To enable direct generation from free-form prompts within a single model, we introduce PlanAudio, an end-to-end framework that maps unconstrained free-form text to unified audio without intermediate prompt preprocessing. To address the challenges of direct free-form generation, PlanAudio integrates latent planning into a single autoregressive framework, predicting continuous audio layout before acoustic token synthesis. We construct a large-scale training corpus and introduce Free-Form Unified Audio Generation Benchmark (FUAG) for evaluation, both spanning speech, sound, and joint speech-and-sound generation. Experiments show that a single PlanAudio outperforms existing pipeline and unified baselines on speech-and-sound generation, while remaining competitive with specialist models on speech and sound generation. Further analysis validates the effectiveness of audio layout planning over textual planning, acoustic planning, and direct generation. Sample Available: https://planaudio.github.io/PlanAudio2026/.

cs.SD · cs.AI · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13684

BypassGym: A Benchmark for Evaluating AI Agents' Endpoint Defense Evasion Capabilities

Weifeng Yuan, Zifan Chen, Yucheng Zhang, Feng Dong, Peng Gao, Haoyu Wang

原文摘要

Artificial intelligence agents increasingly automate cybersecurity tasks, including offensive operations. Frequent attacks across the web make endpoint detection and response (EDR) a critical defense, but whether AI agents can evade it remains unclear. Benchmarks evaluate the offensive capabilities of large language model (LLM) agents in areas such as penetration testing and vulnerability discovery. One important capability that these benchmarks do not address is defense evasion, specifically whether an agent can develop a program that executes a payload without being blocked or terminated by a deployed EDR. We introduce BypassGym, to our knowledge the first benchmark to systematically evaluate this capability on real endpoints. Given command-and-control (C2) shellcode, an agent must generate and compile a loader that executes the payload without being blocked or terminated. A supplementary evasion knowledge skill provides domain knowledge and supports comparisons with and without this input. BypassGym is built on Proxmox VE(PVE), so the evaluation environment runs real Windows systems and EDR products inside virtual machines and returns structured detection feedback to the agent. We evaluate 5 lightweight model backends against 6 EDR products and 3 payloads, and find that even these simple models generate successful submissions, with DeepSeek-V4.1 Flash reaching 17/18 successful runs and Bitdefender allowing only 5 of 15 runs to pass. These results provide a systematic measurement of current AI agents' endpoint defense evasion capabilities and identify areas for defenders to investigate.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.11166

DexForesight: Grounding Dexterous Policies with Implicit Spatial Foresight

Zhongxi Chen, Yi Yang, Xinyi Chen, Mingzhe Ye, Yaping Li, Zhihong Liu, Congsheng Xu, Haonan Wang 等 9 位作者

原文摘要

Dexterous manipulation requires rich spatial foresight over both the surrounding scene and the robot hand. Existing world-action models often acquire such predictive knowledge through explicit future prediction. We introduce DexForesight, a framework that learns spatial foresight implicitly by injecting dynamics-aware world representations into the latent space of a dexterous policy. During training, DexForesight learns an action-conditioned spatial world model from offline scene depth, kinematic hand geometry, and demonstration trajectories. The world model predicts 3D hand-point motion and visual feature changes, capturing both articulated hand dynamics and scene changes under demonstrated actions, including the motion of fingers occluded from the cameras. We then freeze the world model and align policy queries with its predictive world queries, transferring the learned spatial dynamics into policy latents and using them to condition action generation. At deployment, only the policy is retained, operating on RGB observations, language, and robot state without depth input or explicit future prediction. On 11 DexJoCo simulation tasks, DexForesight improves the overall success rate of the base policy from 49.4% to 60.5%. On three real-robot dexterous manipulation tasks, DexForesight achieves 58.3% success, compared with 50.0% for the strongest baseline. Project page https://dexforesight.github.io/

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13677

Deformable Texture Maps: Compiling Dynamic Microgeometry into Neural Textures

Antonio Arian Silaghi, Zhen Chen, Danny Kaufman, Élie Michel, Jean-Marc Thiery, Jorge Lopez-Moreno, Elena Garces

原文摘要

Deformable material surfaces, such as cloth, owe their appearance to fine-scale geometries that restructure under deformation. Even when coarse simulation models efficiently recover this microstructure, explicitly instantiating and rendering it remains expensive. We introduce Deformable Texture Maps (DTM), a representation that compiles deformation-driven microgeometry into a continuous and compact neural texture. Our model maps texture coordinates and local deformations to intrinsic maps describing opacity, surface normals, depth, and material identity. This captures the occlusion and visibility changes generated by deformation. At runtime, these maps provide a common texture-space representation of deformation-driven fine-scale structure that can be evaluated directly on coarse deforming surfaces. Since our representation encodes geometric attributes rather than radiance, lighting and material response remain decoupled from the learned model. This allows us to apply relighting, as well as shading and material editing without retraining. We further introduce a persistent texture-space rendering strategy that caches these intrinsic maps and selectively updates them as deformation changes. This effectively decouples neural evaluation from frame rendering and enables their reuse across frames and conventional rendering effects. We demonstrate our approach on yarn-based textiles, where non-linear material stretch and shear induce changes in yarn geometry, spacing and occlusion. We build our model by, first, sweeping tileable yarn patches through deformation space, then, rendering the detailed geometry into a canonical texture-space, and finally, compressing the resulting maps into a single neural representation. Our resulting model can be evaluated directly on arbitrary coarse garment meshes and integrated with conventional shading pipelines, keeping appearance authorable at render time.

cs.GR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.08743

A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning

Jianhan Zhang, Jitao Wang, John D. Piette, Donglin Zeng, Chengchun Shi, Zhenke Wu

原文摘要

Reinforcement learning (RL) seeks to optimize sequential decisions to maximize population-level benefits over time. However, when deployed in high-stakes settings such as healthcare, RL decisions might systematically restrict some subpopulation's access to valuable services in a manner contrary to the values and goals of stakeholders. Counterfactual fairness (CF) offers a promising framework to address this problem based on causal reasoning. This paper develops a data preprocessing algorithm that, when used in tandem with policy learning, enables CF in RL. Our algorithm relies on a novel quantile distribution mapping method for sequentially estimating the counterfactual states and rewards in the data preprocessing step, subsuming common additivity assumptions used for counterfactual prediction as a special case. We theoretically prove that the per-step level of counterfactual unfairness and infinite-horizon suboptimality gap can be bounded under mild regularity conditions. We also empirically test our algorithm in numerical experiments as well as in application to a real-world interventional digital health dataset.

stat.ML · cs.CY · cs.LG · stat.ME

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13662

NIF-VIO: Visual--Inertial Odometry Across Inertial and Non-Inertial Reference Frames

Zichao Shu, Lijun Li, Zhuochen Li, Zetao Chen, Jianyu Wang

原文摘要

Conventional visual--inertial odometry (VIO) assumes that observations from cameras and an inertial measurement unit (IMU) are jointly consistent in an inertial reference frame. However, this assumption breaks down under non-inertial conditions. Inside accelerating or rotating carriers, visual observations of the interior reflect carrier-relative motion, while IMU measurements contain additional carrier-induced components, which can compromise conventional fusion. We therefore present NIF-VIO for localization relative to the local environment across inertial and non-inertial reference frames. It adapts visual--inertial fusion based on measurement consistency. As inertial conditions change, early constraint isolation is combined with local reoptimization to limit error propagation before inconsistency confirmation and repair the affected local geometry. Under non-inertial conditions, a compact model with temporally shared parameters is introduced to account for carrier motion while preserving inertial information that complements visual constraints. Experiments on public benchmarks and a self-collected dataset demonstrate that NIF-VIO achieves competitive performance under inertial conditions and improved localization accuracy and availability under non-inertial conditions.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.22928

Predicting Risks Before They Happen: Vision-Language Reasoning for Autonomous Driving

Jiaxin Liu, Xiangyu Yan, Liang Peng, Lei Yang, Yuechen Luo, Lingjun Zhang, Hangjun Ye, Guang Chen 等 15 位作者

原文摘要

% Waiting until a driving hazard becomes directly visible can leave an autonomous vehicle little time to respond. Observable precursors, such as another vehicle's unexpected braking, can provide evidence of a hidden hazard and an opportunity for earlier risk mitigation. However, existing driving datasets and language reasoning formulations provide limited supervision for connecting these precursors to potential risks and appropriate responses. We introduce PotentialRiskQA, an ego-view driving dataset that marks precursor and risk appearance times and provides structured annotations linking scene evidence, potential risks, and mitigation actions. Building on this dataset, PR-Reasoner trains vision-language models to generate a precursor-centered reasoning chain through scene-level supervision and auxiliary tasks for scene understanding, precursor identification, risk inference, driving suggestions, and meta-actions. Experiments across multiple backbones show improved scene understanding, risk cognition, and action quality, with fewer unsupported risk claims on most models. Ablations show that supervising intermediate reasoning steps further reduces unsupported risk claims and improves action quality beyond scene-level training alone. On NAVSIM, the trained representations improve open-loop planning performance, demonstrating benefits beyond language-based evaluation.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.33135

MpFA: Hardware-Efficient Train-Free QK4V8 FlashAttention Kernels on Blackwell GPUs

Chencheng Deng, Jianbin Fang, Dezun Dong

原文摘要

Long-context LLM inference pushes modern GPU serving stacks into an attention-bound regime, where both compute and memory are dominated by the softmax-GEMM pipeline. On NVIDIA Blackwell GPUs, FP4 Tensor Cores offer high matmul throughput, but we find that fully FP4 attention often fails to translate this throughput into end-to-end speedups due to non-matmul costs: online quantization after softmax, tensor/shared-memory data movement, and contention on the softmax path. We present MpFA, a training-free FlashAttention kernel optimized for Blackwell. Guided by hardware characterization, MpFA uses mixed precision: NVFP4 for QK and FP8 for PV (QK4PV8). This preserves low-bit QK throughput while avoiding the conversion and scaling overheads of FP4 PV. To recover accuracy without further stressing the softmax pipeline, MpFA introduces rank-one smoothing compensation implemented as an additional Tensor Core MMA. MpFA further improves performance with a fine-grained asynchronous pipeline, tensor-memory reuse, and adaptive parallel partitioning across prefill and decode. On an NVIDIA SM100 GPU and across 16K-128K contexts, MpFA improves prefill throughput over state-of-the-art BF16/FP8 baselines and increases end-to-end output throughput by 2.81$\times$ over BF16 FA4 across Llama-3.1-8B and Qwen3-14B. Across five benchmark suites and two models, rank-one compensation recovers 62.5% of the accuracy loss with about 2.0% kernel overhead.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13649

Predicting Single-Cell Spatial Gene Expression from H&E using Pathology Foundation Models

Ashley P. Tsang, Evan Liu, Conrad Foo, Bob Chen, Hassaan Maan, Donny Chan, Elisa Penna, Veronica Ibarra Lopez 等 13 位作者

原文摘要

Predicting gene expression directly from hematoxylin and eosin (H&E) images offers a scalable and cost-effective approach to deriving spatial molecular insights from routine histology. Pathology foundation models, typically pretrained using tile-level objectives, may not adequately capture the fine-grained cellular features required for single-cell molecular prediction. In this work, we systematically evaluate and establish domain-informed and task-specific strategies for adapting pathology foundation models to single-cell spatial gene expression prediction. Using paired H&E and Xenium 5K data, our approach improves mean gene-wise Pearson correlation by 2.67x over the standard zero-shot baseline and achieves state-of-the-art performance on this task. We demonstrate strong gene-detection performance across the full gene panel (AUROC = 0.800) and importantly identify that predictive performance varies substantially across biological contexts, with our model achieving its highest Pearson correlation for spatially variable genes (r = 0.643). Our findings demonstrate that, despite the challenges of sparse single-cell measurements, H&E-derived representations can recover biologically meaningful spatial expression patterns, highlighting both the opportunities and limitations of morphology-based molecular inference at cellular resolution.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13644

Tri-Stack-PIM: A Heterogeneous Multi-Die 3D-Integrated Processing-in-Memory Architecture for Efficient LLM Inference

Shuao Jia, Zichao Ling, Chen Bai, Kang Zhao, Jianwang Zhai

原文摘要

Large language model (LLM) inference during autoregressive decoding is fundamentally memory-bandwidth-bound. Existing three-dimensional (3D) stacked processing-in-memory (PIM) architectures alleviate the off-chip bandwidth wall, yet remain constrained by several limitations: physical layer (PHY), controllers, and direct memory access (DMA) engines compete with arithmetic units for advanced-node base-die area; hybrid-bonding keep-out zones fragment the base-die floorplan and impede physical design; compute tiles built in a dynamic random access memory (DRAM) process are restricted to low logic density, capping achievable throughput; and nonlinear operations offloaded to the host reintroduce a communication bottleneck. We present Tri-Stack-PIM, a heterogeneous three-die architecture that resolves these tensions by dedicating a top DRAM die to storage, a middle buffer die on a mature process node to PHYs, DMA engines, and near-memory compute units, and a bottom base die on an advanced node exclusively to single-instruction-multiple-data (SIMD) multiply-accumulate (MAC) arrays. We further introduce a Unified Buffer-Aware Ready-based (UBAR) hardware out-of-order micro-operation scheduler that enforces cross-die dependency correctness at runtime while maximizing issue-slot utilization through a multi-term priority function, recovering up to 90% of the cycles that an in-order policy would forfeit. A joint architecture-dataflow co-exploration identifies Pareto-optimal configurations that reduce the energy-delay product (EDP) by 33-66x over naive parameter selection. Evaluated across multi-head, grouped-query, and multi-query attention (MHA, GQA, MQA) mechanisms with batch sizes 1-32, Tri-Stack-PIM achieves geometric-mean speedups of 4.95x, 2.57x, and 1.68x across all three attention mechanisms and batch sizes over a graphics processing unit (GPU) baseline, AttAcc, and H2-LLM, respectively.

cs.AR

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.31303

The Value of Memory in Language Agents

Zehao Lin, Erenkai Zhu, Helan Hu, Xixuan Hao, Chunyu Li, Linyun Liu, Kai Chen, Chenxu Du 等 9 位作者

原文摘要

Language agents carry information from past experience into future tasks whose demands may be unknown or change over time. We develop a decision-theoretic account of the capability available in history, retained in storage and realized through use, together with the costs of recovery and revision. Drawing on comparison of statistical experiments, information acquisition and the value of computation, we express these values in units of avoided task loss. Recovery and revision define attainable risk-cost profiles and preservation across uses. A cost-aware simulation condition and five minimal counterexamples establish how information timing, access, computation, feedback and demand shape these comparisons; a pointer with zero answer mutual information can reduce the cost of exact recovery. Allocation and recovery criteria identify when gains pay for estimation, selection and evidence access. Complete-split studies on LoCoMo, LongMemEval-S and PersonaMem cover 3,075 questions and two readers. They show complementary value from saved state and recovered evidence, task-dependent performance orderings, and the role of reuse in amortizing construction costs. Multi-project workflows connect correction to preservation and validation costs. Together, the theory and studies support decisions about what to retain, which recovery options to preserve, and how to adapt memory as evidence and objectives evolve.

cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.08896

OmniReview: A Large-scale Benchmark and LLM-enhanced Framework for Realistic Reviewer Recommendation

Yehua Huang, Penglei Sun, Zebin Chen, Zhenheng Tang, Xiaowen Chu

原文摘要

Academic peer review remains the cornerstone of scholarly validation, yet the field faces some challenges in data and methods. From the data perspective, existing research is hindered by the scarcity of large-scale, verified benchmarks and oversimplified evaluation metrics that fail to reflect real-world editorial workflows. To bridge this gap, we present OmniReview, a comprehensive dataset constructed by integrating multi-source academic platforms encompassing comprehensive scholarly profiles through the disambiguation pipeline, yielding 202, 756 verified review records. Based on this data, we introduce a three-tier hierarchical evaluaion framework to assess recommendations from recall to precise expert identification. From the method perspective, existing embedding-based approaches suffer from the information bottleneck of semantic compression and limited interpretability. To resolve these method limitations, we propose Profiling Scholars with Multi-gate Mixture-of-Experts (Pro-MMoE), a novel framework that synergizes Large Language Models (LLMs) with Multi-task Learning. Specifically, it utilizes LLM-generated semantic profiles to preserve fine-grained expertise nuances and interpretability, while employing a Task-Adaptive MMoE architecture to dynamically balance conflicting evaluation goals. Comprehensive experiments demonstrate that Pro-MMoE achieves state-of-the-art performance across six of seven metrics, establishing a new benchmark for realistic reviewer recommendation.

cs.IR · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13628

DepthGS: Depth-Guided Gaussian Splatting with Adaptive Regularization for High-Quality Surface Reconstruction

Xiao Ren, Yu Liu, Ning An, Jian Cheng, Xin Qiao, You-Fu Li, He Kong

原文摘要

Recently, 3D Gaussian Splatting (3DGS) has emerged as an efficient representation for novel view synthesis, offering fast differentiable rendering and high visual fidelity, and has increasingly been extended to surface reconstruction. However, the relationship between depth information and reconstruction error remains insufficiently explored, limiting the principled design of geometric optimization strategies. To fill the above gap, we propose DepthGS, a depth-guided Gaussian splatting framework. We derive theoretical relations between depth discrepancies and reconstruction error under single-view and multi-view settings, and rigorously establish corresponding upper bounds on reconstruction error. The above analysis informs the design of adaptive regularization strategies in both single-view and multi-view optimization. More specifically, for single-view optimization, we propose a depth-difference-guided adaptive normal regularization strategy that modulates the normal constraint strength using a geometric reliability weight computed from local depth differences. For multi-view optimization, we introduce a cross-view depth consistency regularization strategy that promotes consistency between neighboring-view depth maps and the corresponding depth predictions obtained by transforming the current view's depth predictions into those neighboring views. Extensive experiments on DTU, Mip-NeRF360, and Tanks and Temples demonstrate that DepthGS achieves competitive performance in both surface reconstruction and novel view synthesis.

cs.CV

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版本更新 · v3arXiv:2610.11302

iCATS: Fast Video Generation via Interaction-Aware Sparse Attention and Timestep-Adaptive Sparsity

Chengfeng Han, Ang Wang, Baole Ai, Xianlu Bian, Jie Yao, Zilong Huang, Dandan Ding

原文摘要

Training-free sparse attention offers a practical acceleration solution to Diffusion Transformers (DiTs) via reducing computations without fine-tuning. It typically involves estimating the importance of query-key regions and deriving sparse masks to compute only the important candidates, which inevitably introduces approximation errors that may degrade generation quality. To better balance the efficiency-quality trade-off, we propose iCATS, integrating improved importance estimation and sparse mask construction with an efficient hardware execution strategy. Specifically, for importance estimation, unlike previous works that perform independent clustering over query and key tokens based on feature similarity to estimate attention scores, iCATS demonstrates that clustering based on query-key dot-product interactions is more accurate and further reformulates this objective as a simple quadratic form for low-cost computation. For sparse mask construction, instead of using a fixed top-p rule, we observe that tolerance to sparse approximation errors varies across denoising timesteps and therefore introduce an SNR-guided sparsity schedule to adjust sparsity dynamically, leading to higher accuracy. Finally, for hardware execution, we devise a tail-merging strategy to reduce padding overhead caused by irregular cluster sizes, improving GPU kernel utilization. Extensive experiments show that iCATS achieves $2.03\times$ acceleration with 31.017 dB PSNR on HunyuanVideo-T2V-13B and $1.55\times$ acceleration with 29.301 dB PSNR on Wan2.1-T2V-14B, delivering a state-of-the-art efficiency-quality trade-off.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13620

When to Start Denoising? Adaptive Denoising Entry for Generative Video Compression

Zifu Zhang, Yuqiao Li, Ding Ding, Shengxi Li, Mai Xu, Daowen Li, Kai Li, Ying Chen

原文摘要

Generative video compression offers a promising solution for compressing and reconstructing videos under extremely low bitrate constraints, among which the diffusion-based solutions still achieve the best performance by providing the powerful generative latent prior. However, their effectiveness critically depends on when the compressed latent is introduced into the diffusion process, which directly governs the trade-off between reconstruction fidelity and perceptual realism. To address this limitation, we identify denoising start-timestep selection as a key factor governing this trade-off and formulate it as a geometric optimization problem. Based on this formulation, we propose a novel adaptive diffusion entry method for generative video compression (ADE-GVC), the first timestep-aware generative video compression framework, featuring a lightweight predictor that estimates an adaptive denoising start point from the compressed latent. A temporal adapter further extracts compression-aware conditions and injects them into a pretrained video diffusion model, enabling the reconstruction of videos with both high fidelity and realism. More importantly, we observe a strong quantitative relationship between the predicted timesteps and the distortion between the original and compressed latents, providing empirical support for our optimization formulation. Extensive experiments demonstrate that ADE-GVC achieves the state-of-the-art performance on perceptual metrics and substantially improves reconstruction realism over existing generative video compression methods. Notably, our ADE-GVC achieves an average bitrate savings of 40.99\% (on DISTS) and 95.24\% (on PSNR) compared to the latest open-source baseline GLC-Video.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2610.16717

Where Is the "Self" of a Large Language Model? -- From Self-Index to Self-Reference

Yuchen Li, Xintong Li, Zongqi Fan, Ken-tye Yong, Nguyen H. Tran

原文摘要

Whether a large language model has a self is usually argued without a way to measure it. We measure a functional self with two levels familiar from biology: a self-index, which marks material as the model's own, as animals tag self-generated signals with a corollary discharge, and self-reference, the ability to recognize what is one's own, as in mirror self-recognition. Our main finding is that language models mark their own messages internally and that recognizing them depends on this mark: no model we tested recognizes its own messages without it, and in the four models we edited, changing the mark removes or creates the recognition. With the same text placed in an Assistant, a User, or a System message of its chat template, Qwen3.8-27B separates its own message from both external ones at all 64 layers and in every held-out text. Asked who wrote a message, it answers "you" with probability 0.77 for its own messages and at most 0.07 for the others. Setting eight coordinates per layer, along which its own message differs most from the same text as a User message, to their User values lowers the rate of "you" to at most 0.05, and the reverse edit makes the model claim a User message, closing at least 41% of the gap. Random directions do neither, and answers about the content stay unchanged. This holds in Qwen3.8-27B without and with thinking, gpt-oss-20b, gpt-oss-120b, and MiMo-V2.6-Distill-Qwen-9B. Across twenty-five open-weight models from fourteen developers, the two levels are nested: no model recognizes its own messages without a self-index (permutation p = 0.004). Without any question, next-token prediction on the model's own message differs from that on the same text in another role, more so as more layers carry the self-index (Spearman rho = 0.82), and the same coordinates carry this difference. These results support the account that self-reference is a readout of a self-index.

cs.CL

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新论文 · v1arXiv:2611.13615

Synapse-Control: Making Agent Experience Actionable with Lightweight Runtime Control

Zhiyuan Huang, Shengan Zheng, Chenghu Yang, Song Zhang, Lingyun Li, Chen Yu, Weiquan Huang, Linpeng Huang

原文摘要

Large language model agents reuse execution experience to improve reliability on complex tool-based tasks. Systems such as Sentry call large models to generate failure diagnoses before retrieving advice, adding inference cost to experience reuse. We present Synapse-Control, an agent runtime whose lightweight control plane selects advice and determines when to send it to the task-executing model. Rules shortlist relevant advice, and a lightweight 1.7B classifier fine-tuned on teacher-labeled state-suggestion pairs scores those candidates instead of generating a diagnosis. Micro-contracts encode reusable interface checks, such as required arguments or valid options, without prescribing complete task programs. A Prompt Value Table records earlier guidance and feedback to limit repeated suggestions through cooldown and token-budget checks. Across 1,308 attempts per method in AppWorld and WebShop, Synapse-Control achieves 270 successes (29.8% more than Base), replaces 4,987 generative diagnosis calls, and reduces auxiliary control time relative to Sentry by 50.2% and 51.0%, respectively.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13614

Emilia2: A Large-Scale Dataset for Controllable Long-Form Speech Generation

Qinke Ni, Dekun Chen, Junwen Qiu, Huan Liao, Xu Li, Xingwen Han, Wan Lin, Kunyu Feng 等 17 位作者

原文摘要

Generating long-form speech requires models to keep content, speaker identity, turn-taking, and expressive style consistent over time. Yet most open training corpora contain examples lasting only tens of seconds and open long-form benchmarks cover at most two speakers per recording. We introduce Emilia2, a 229K-hour multilingual dataset for controllable long-form speech generation. From in-the-wild data, Emilia2 constructs short utterances, single-speaker long-form speech, and multi-speaker dialogues, preserving transcripts, speaker identities, pauses, and acoustic transitions. Examples longer than five minutes account for 50.0% of single-speaker long-form hours and 66.8% of dialogue hours. To support expressive control, Emilia2 provides natural language instructions for 4.77M examples and nonverbal-vocalization tags for 933K examples. We also introduce Emilia2-testset, 23 hours of bilingual recordings of up to 30 minutes and four speakers, each speaker paired with reference audio. We train autoregressive (AR) and non-autoregressive (NAR) models on Emilia2. Our 0.6B-backbone NAR model achieves the lowest Chinese CER and English WER on TTSD-Eval (5.27%/9.93%) among compared systems, while our AR model is on par with Qwen3-TTS-12Hz-VD on InstructTTSEval (zh/en: 76.2/78.3 vs. 77.1/77.9). These results validate Emilia2 as a comprehensive data foundation for long-form speech generation and minute-scale expressive control. Demo: https://charlesnii.github.io/Emilia2-demo/.

eess.AS · cs.SD

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新论文 · v1arXiv:2611.13613

Megrez: A Knowledge-Guided Agent for Formal Mathematics over MK Set Theory

Tiancheng Xu, Wensheng Yu

原文摘要

General-purpose coding agents equipped with proof-assistant tools have shown strong performance on standalone competition problems, yet cumulative textbook formalization inside a low-resource mathematical library still faces three obstacles: aligning textbook concepts with library declarations, keeping long-horizon search synchronized with live proof states, and integrating locally verified proofs reliably into a growing codebase. We present Megrez, a knowledge-guided formalization agent for Rocq libraries built on Morse--Kelley (MK) set theory. Megrez coordinates a general coding agent through three mechanisms: (1) a source-rebuildable knowledge graph merging formal declarations, compiler references, and curated metadata to support retrieval via Chinese aliases, concept clusters, proof dependencies, and multi-lane full-text ranking; (2) a feedback-driven 1--5 tactic short-burst proving loop paired with a goal-local branch ledger and exponential-interval structured reflection; and (3) a three-check acceptance pipeline (statement fidelity, axiom auditing, and full builds) coupled with a multi-corpus Provider stack and a candidate-bound recoverable batch state machine. Deployment and evaluation on a point-set topology corpus demonstrate effective cross-chapter formalization and scalable premise retrieval.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13612

Earth Embeddings in Environmental Remote Sensing

Yuchi Ma, Jiaqi Yang, Mengyu Liang, Ivan Higuera-Mendieta, Caleb Robinson, Anthony Ortiz, Min Chen, David B. Lobell 等 10 位作者

原文摘要

Earth observations from satellite platforms have profoundly transformed research and applications across numerous domains. However, uneven observation coverage and frequent data gaps continue to limit their ability to capture Earth's surface dynamics. Moreover, processing multi-modal Earth observations requires substantial computational resources and sophisticated data harmonization frameworks, which may not be readily accessible to many remote sensing practitioners. Earth embeddings have emerged as promising solutions to these challenges. As general-purpose representations converted from multi-source Earth observations, Earth embeddings can support a wide range of downstream tasks with no requirement of task-specific feature design and preprocessing. In this study, we first introduce the theoretical foundations of Earth embeddings. Then, we review several representative Earth embedding products. We next examine the applications of Earth embeddings across six major domains of environmental remote sensing. Finally, we systematically assess the performance of Earth embeddings in four downstream tasks, including hyperspectral image classification, individual tree crown segmentation, crop yield prediction, and aboveground biomass mapping. Our review and case studies suggest that, despite several advantages over conventional Earth observation data, current Earth embeddings do not always outperform remote sensing imagery or hand-crafted features. Earth embeddings should not be viewed as a direct replacement for conventional EO data, but rather as representations that provide stable information across space and time. Remote sensing practitioners may consider evaluating Earth embeddings' suitability for specific tasks and using them strategically, rather than assuming superior performance by default.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.05947

LucidNFT: LR-Anchored Multi-Reward Preference Optimization for Flow-Based Real-World Super-Resolution

Song Fei, Tian Ye, Sixiang Chen, Zhaohu Xing, Jianyu Lai, Lei Zhu

原文摘要

Generative real-world image super-resolution (Real-ISR) can synthesize visually convincing details from severely degraded low-resolution (LR) inputs, but stochastic sampling can introduce semantic or structural hallucinations unsupported by the LR evidence. Sampling multiple outputs per LR input exposes trade-offs between perceptual quality and faithfulness that reinforcement learning (RL) can optimize beyond paired supervision. Effective alignment is limited by the lack of a degradation-robust LR-referenced faithfulness signal, the loss of per-objective reward contrasts when rewards are combined before normalization in Diffusion Negative-aware FineTuning (DiffusionNFT), and limited coverage of real degradations. We propose LucidNFT, a multi-reward RL framework for flow-matching Real-ISR. LucidNFT introduces LucidConsistency, a degradation-invariant LR-referenced evaluator trained with content-consistent degradation pools and hard negatives created by inpainting to detect localized hallucinations. Its decoupled reward normalization strategy normalizes each reward within each LR-conditioned rollout group before fusion, preserving per-objective contrasts across candidates. LucidLR provides a large-scale collection of real-world degraded images for RL fine-tuning. Experiments show that LucidNFT improves perceptual quality on strong flow-based Real-ISR baselines while generally maintaining LR-referenced consistency across diverse real-world scenarios. In a reward-independent human study, LucidNFT outputs were judged more faithful to the LR inputs in 72.5 percent of comparisons.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.11824

When to Trust Adaptation: Contextual Fusion for Transferable Vision-Language Prompting

Zhenyuan Chen, Wenshuo Tao, Zhuo Huang, Shuo Chen, Ming-Ming Cheng, Xiang Li

原文摘要

When a vision-language model encounters a new class or domain, how much should it trust its learned prompts? Because source adaptation can correct pretrained errors on some target images while disturbing valid predictions on others, we introduce Contextual Adaptive Fusion (CAF), which retains four prediction branches by pairing frozen and prompted visual and textual representations. To arbitrate when branches disagree on an image, CAF combines each branch's predictive confidence with how frequently the branches favor its proposed class across the unlabeled target collection, placing greater trust in predictions that are locally confident yet less dominated by collection-wide frequency bias. The fusion coefficients are learned once on source data and remain fixed at inference. Trained on ImageNet, CAF achieves 69.96% average accuracy across ten cross-dataset benchmarks (1.92 percentage points above ManiPT) and remains highly competitive across four ImageNet variants, setting a new best of 52.21% on ImageNet-S. In base-to-novel transfer across eleven datasets, CAF further reaches 79.77% novel-class accuracy and an 82.81% harmonic mean. Code is available at https://github.com/zhenyuanchenai/CAF.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.10564

SplitCom: Communication-efficient Split Federated Fine-tuning of LLMs via Temporal Compression

Tao Li, Yulin Tang, Yiyang Song, Cong Wu, Xihui Liu, Pan Li, Xianhao Chen

原文摘要

Federated fine-tuning of on-device large language models (LLMs) mitigates privacy concerns by preventing raw data sharing. However, the intensive computational and memory demands pose significant challenges for resource-constrained edge devices. To overcome these limitations, split federated learning (SFL) emerges as a promising solution that partitions the model into lightweight client-side and compute-intensive server-side sub-models, thus offloading the primary training workload to a powerful server. Nevertheless, the high-dimensional activation exchanges in SFL lead to excessive communication overhead. To overcome this, we propose SplitCom, a communication-efficient SFL framework for LLMs that exploits temporal redundancy in activations across consecutive training epochs. Inspired by video compression, the core innovation of our framework lies in selective activation uploading only when a noticeable deviation from previous epochs occurs. To balance communication efficiency and learning performance, we introduce two adaptive threshold control schemes based on 1) bang-bang control or 2) deep deterministic policy gradient (DDPG)-based reinforcement learning. Moreover, we implement dimensionality reduction techniques to alleviate client-side memory requirements. Furthermore, we extend SplitCom to the U-shape architecture, ensuring the server never accesses clients' labels. Extensive simulations and laboratory experiments demonstrate that SplitCom reduces uplink communication costs by up to 98.8\,\% in its standard configuration and total communication costs by up to 95.8\,\% in its U-shape variant without noticeably compromising model performance.

cs.NI

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