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新论文 · v1arXiv:2610.35550

Cross-Domain Sequential Recommendation with Representation-Level User Transfer

Abdulaziz Samra, Nikita Sukhorukov, Alexey Grishanov, Alexey Vasilev, Evgeny Frolov

原文摘要

Cross-domain sequential recommendation aims to improve next-item prediction in a target domain by leveraging user behavior from auxiliary source domains. However, real-world deployment faces two critical challenges: business constraints that isolate interaction data across domains, and user heterogeneity, where many users lack cross-domain activity. This makes existing cross-domain methods difficult to apply. In this paper, we propose a simple and practical framework for cross-domain sequential recommendations that transfers information exclusively through learned user representations, without sharing raw interaction data or item embeddings across domains. We introduce a lightweight adapter that projects the source-domain user representation into the target-domain embedding space and integrates it with the target interaction sequence. Importantly, when source data is unavailable (i.e., the user observed only in the target domain), the model seamlessly defaults to a robust target-only sequential recommender, retaining benefits from joint training across all users. Extensive experiments were conducted under two setups; one targets benchmarking against previous baselines, while the other focuses on realistic and scalable conditions. Both sets of experiments demonstrate consistent performance gains for shared and unshared users.

cs.IR · cs.AI · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35549

Explainable Prediction of High-Speed Solar Wind Streams driven by Coronal Holes via Topological Data Analysis

Francesco Marchetti, Sabrina Guastavino, Daniel Collin

原文摘要

High-speed solar wind streams (HSSs) originating from coronal holes (CHs) are an important driver of recurrent geomagnetic activity. Since HSSs are associated to coronal structures visible in extreme-ultraviolet observations, this provides a natural basis for forecasting several days in advance. In this work, we investigate whether the topology of coronal structures observed in extreme ultraviolet (EUV) images contains predictive information on the subsequent occurrence of CH-driven HSSs at Earth. To this end, we consider Topological Data Analysis (TDA), and in particular persistent homology, to classify EUV solar images according to the occurrence of HSSs at Earth four days later. The target is a binary classification of HSS occurrence at a four-day lead time, defined using an HSS interval catalog and solar wind speed thresholds. Several combinations of topological representations and classifiers are evaluated under a common cross-validation framework. The results indicate that the topology of EUV coronal structures alone contains predictive information on subsequent CH-driven HSS occurrence, despite the absence of explicit CH geometric descriptors in the model input. To investigate the origin of this predictive information, we develop a model-level explainability framework based on cross-validation-aggregated feature weights and spatial evidence maps. A fully traced example connects persistence diagrams and persistence-image features to the classifier output and to their spatial localization in the original EUV observation. The proposed framework provides a transparent and computationally lightweight approach for investigating the relationship between coronal topology and subsequent high-speed solar wind streams.

astro-ph.SR · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35545

Efficient probabilistic weather forecasting at quarter-degree resolution with two-sided transport attention

Junu Choi, Youngjoon Hong

原文摘要

High-resolution probabilistic weather forecasting remains computationally demanding. Here we present Astra, a 0.25$^\circ$ ensemble model trained on eight H200 GPUs. It combines Kantorovich Attention, a two-sided transport attention in which each region chooses where it reads from and writes to, so every region sends and receives at token resolution, with a self-featurised continuous ranked probability score (CRPS) that also scores forecasts in the model's multiscale features. In WeatherBench 2, Astra matches GenCast's CRPS averaged over five upper-air targets and days 1-10 with 9.8% of its hardware-normalised training compute. Verified against ERA5, its CRPS and ensemble-mean root-mean-square error are lower than the ECMWF ensemble's on all 600 variable, level and lead-time combinations, and its CRPS is lower than GenCast's on 73.5% of them. A 15-day member needs 39 times fewer floating-point operations than GenCast's GPU implementation, and 50 members take under 40 min on one consumer GPU, within 21 GB.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35544

StateLoop: Persistent State for Looped Transformers

Feichen Song, Boyi Zeng, He Li, Shixiang Song, Yiqin Hao, Zitong Wang, Yizhou Yang, Yifan Liu 等 10 位作者

原文摘要

Looped Transformers offer a parameter-efficient approach to language modeling by repeatedly applying a shared backbone. Vanilla looped Transformers use each backbone output directly as the input to the next iteration. These outputs may contain information useful several iterations later, but retaining it across the loop depends entirely on the single hidden state. We introduce StateLoop, which maintains one additional persistent state per token to retain information across iterations. At each iteration, a learned gate controls how much of the previous state is retained and how much information from the backbone output is incorporated. The updated state is projected back and added to the backbone output for subsequent iterations and final prediction. This design reduces Pile perplexity and improves average accuracy across eight zero-shot tasks over Vanilla Looped. Its perplexity advantage over Vanilla Looped grows with trained loop depth, consistent with persistent state becoming increasingly useful as recurrent computation deepens.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35543

Tiny Suffix Arrays

Sharma V. Thankachan

原文摘要

Suffix array and inverse suffix array queries on a text of length $n$ can be answered in $\widetilde{O}(τ^3)$ time using an auxiliary data structure occupying $O(n/τ)$ words, for any $τ\in [1,n]$.

cs.DS · cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35540

Beyond Modeling Order: Dual-Path Spatiotemporal Representation Learning for Cross-Subject EEG Emotion Recognition

Zehui Xiao, Yi Zheng, Guixing Fu, Wenkai Lu, Lingfei He, Tao Tan, Yue Liu, Silun Wang 等 10 位作者

原文摘要

Cross-subject generalization remains a major challenge for EEG-based emotion recognition due to pronounced inter-subject variability in neural activity and emotion-related spatiotemporal patterns. Existing methods mainly alleviate inter subject differences through distribution alignment or contrastive learning, while paying limited attention to representation bias associated with fixed spatiotemporal modeling orders. We hypothesize that high-level emotion representations of the same EEG signal should remain consistent across different spatiotemporal modeling paths. Based on this hypothesis, we propose a Dual-Path Spatiotemporal Contrastive Learning (DP-STCL) framework for cross-subject EEG emotion recognition. DP-STCL employs two complementary encoders with independent parameters, namely a Temporal-to-Spatial (TS) path and a Spatial to-Temporal (ST) path, to model EEG spatiotemporal information under different integration orders. During representation learning, the same emotional stimulus is used to establish cross subject associations, while three complementary relations jointly constrain representation variations across subjects and modeling paths. This promotes stable and discriminative stimulus-related emotion representations. To further handle limited labeled target subject data, we introduce a LoRA-based few-shot adaptation strategy with low parameter overhead. Extensive cross-subject experiments on SEED, SEED-IV, SEED-V, and FACED show that DP-STCL consistently outperforms the compared methods, demonstrating its effectiveness in improving cross-subject EEG emotion recognition.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.22433

VIREL: Route-Local Lattice Residual Compression for Exact and Error-Bounded Floating-Point Time Series

Yue Zhang, Jiatao Lin, Haopeng Chen

原文摘要

Time-series databases store floating-point columns in independent pages that are written at ingest, rewritten at compaction, and decoded on every scan. Some columns must return the stored IEEE 754 word; others accept a pointwise bound $\varepsilon$. Codecs for both contracts turn each value into an integer on a lattice, but a real page mixes decimal resolutions. Keeping one scale per block charges every coarse step for the finest value: one scale-6 reading in a 1,024-value page cuts Falcon's compression ratio by 36%. We present VIREL, route-local lattice residual compression. A value enters a lattice only after the encoder has replayed the decoder's arithmetic on it. VIREL-Exact replays the decoder's division at each value's least decimal scale, which we prove always succeeds below $2^{51}$, gives every resolution its own lane and history, and places averages on a rational lattice; the same finer value costs it 0.6%. VIREL-EB replays a fused multiply-add on an error lattice, so every admitted value decodes within $\varepsilon$. One integer coder serves both contracts. On 14 canonical streams, VIREL-Exact emits 27.6% fewer bytes than Falcon. Fast beats ALP on ratio and both speeds at once: 7.01$\times$ against 4.19$\times$, 476 against 120 MB/s, and 5.0 against 4.5 GB/s on one core. Upper is the smallest output on all 48 larger streams. Ingest with Fast and compaction with Upper store those streams in Apache TsFile at 48.7% of ALP's bytes. VIREL-EB beats Machete on ratio and both speeds in all 90 dataset-bound cells.

cs.DB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35536

EARS: Evidence-Aware Retrieval via Sound and Motion for Budgeted Video Reasoning

Shih-Yao Lin

原文摘要

Uniform frame sampling can miss brief evidence in sparse-event videos when the VLM sees only K frames. We propose EARS (Evidence-Aware Retrieval via Sound and motion), a training-free, query-free front-end that ranks frames using optical-flow motion and audio energy, then applies temporal non-maximum suppression for diversity. The evidence score alone has frame-level AP 0.39; used only to select frames for the VLM, EARS with temporal diversity reaches localization AP 0.94, compared with 0.88 under Uniform sampling. In the updated matched study, at K = 1 EARS reaches XD-subset F1 0.660 versus 0.614 for centered Uniform (the paired interval includes zero); earlier backbone studies use separate protocols. On mostly-silent UCF-Crime, however, EARS slightly trails Uniform. A single configuration is used across both benchmarks; the new controls do not repeat the original held-out search. EARS therefore targets low-budget videos whose salient evidence aligns with motion or audio; on dense narratives or stealthy events, uniform or semantic selection remains preferable, and cheap modality statistics cannot reliably route between them. Code: https://github.com/mikeslin-ir/EARS

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35533

An Algorithmic Account of Few-Shot Concept Induction in Language Models

Michael A. Lepori, Laila Johnston, Aalok Sathe, Zhuonan Yang, Roman Feiman, Ellie Pavlick

原文摘要

Both models and minds not only encode features that describe an entity, but can specifically represent which features of that entity are relevant for a given context. This core capability underlies in-context learning in language models and a variety of cognitive phenomena in minds, yet it is unclear how intelligent systems identify and promote relevant features. In this work, we propose Amortized Direction Accentuation (ADA), an algorithmic hypothesis that provides an explanation for how relevance is determined in the context of few-shot concept induction --- the ability to infer a latent concept from a small number of positive examples. Under ADA, concept induction proceeds by forming a summary representation of the examples in a feature space that is shaped by amortized knowledge about which features tend to be useful for finding commonalities between items. We investigate and validate ADA in language models using a few-shot concept induction task. This work provides a novel perspective on the mechanisms supporting contextualization in language models and a promising hypothesis for a core aspect of cognition.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.29924

DCGrasp: Distance-aware Controllable Grasp Generation

Hiroyasu Akada, Jesús Pérez, Emre Aksan, Vasileios Choutas, Cristian Romero, Alberto Garcia-Garcia, Vladislav Golyanik, Christian Theobalt 等 9 位作者

原文摘要

Generating 3D hand-object interactions is essential for applications in robotics, AR/VR, and synthetic data generation, where flexible controllability and strong generalization to diverse object geometries are required. However, existing methods rarely satisfy these requirements, limiting their practical applicability. We present DCGrasp, a multi-modal diffusion framework for distance-aware controllable grasp generation. DCGrasp is built on a novel grasp energy term that computes Distance Profile, a signed distance from each hand vertex to the nearest object point, coupled with distance-aware weighting, effectively capturing the semantically similar hand-object interaction in near-contact regions while remaining invariant to object and hand identity. Given various controllable signals, DCGrasp first generates a Distance Profile based on a Diffusion Transformer, together with a corresponding candidate hand pose. We then refine the candidate pose through optimization, enforcing consistency between the optimized hand pose and the generated Distance Profile in near-contact regions. Our experiments show that DCGrasp produces high-quality, geometrically plausible grasps with flexible user control, generalizing to diverse object and hand shapes and scales. Our work establishes a robust and versatile pipeline for the synthesis of controllable 3D hand-object interactions. The supplementary video with dynamic visualization is available on our project page.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35531

One Loss Resolves Signed Rank-One Gradient Fibers: Exact Decoding on Cycle and Theta Blocks

Junwen Lou, Shuo Tian, Penghui Liu

原文摘要

For masked rank-one least squares with unrestricted signs, gradients reveal weighted margins and the loss reveals the target's squared norm. We prove that this one scalar resolves the gradient ambiguity on bipartite cycle and length-three parallel-path blocks: every target fixed before sampling and nonzero on cycle edges is identified almost surely by one legal positive query drawn from an absolutely continuous law. On six-cycle flowers, open target and query sets retain $3^r$ bounded gradient candidates. A common algebraic witness excludes cross-block energy cancellation. Cactus masks admit explicit exception certificates and target-uniform finite-grid bounds; theta blocks admit an existence guarantee. For nondegenerate cactus reductions, an exact decoder returns complete actual real lists, with bit cost polynomial in input length, the cycle-degree product $D$ and the optional budget parameter $2^κ$. Energy-coordinate propagation certifies singletons without a product join. The product $D$ can grow exponentially in cycle count. Exponential certificate-degree and output bounds, and weak NP-hardness at a fixed uniform query, characterize distinct costs of exact decoding.

cs.SC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35530

CANS: Capability-Preserving Neuron Steering for Large Language Models

Yucheng Wu, Chengyi Xie, Chenpeng Wang, Liangming Pan

原文摘要

Activation steering offers training-free and dynamic control of target behaviors in large language models, but existing methods can impair general capabilities and require domain-specific intervention designs. We introduce Capability-Preserving Neuron Steering (CANS), a unified framework that balances target-behavior control with capability preservation. To make target-behavior improvement and general-capability preservation tractable, we introduce two proxies: log-probabilities of target tokens in model generated responses, and output-distribution KL divergence on general-purpose inputs. Combining the local approximation of the two proxies yields a closed-form solution, enabling lightweight steering without iterative model training or additional inference-time computation. The resulting neuron-wise scaling coefficients achieve effective steering while limiting general-capability loss. A shared data-construction and intervention procedure supports adaptation across domains using sampled responses with target-behavior filtering and target-span annotations. Across safety, toxicity, and hallucination control on Qwen and Llama models, CANS achieves comparable or better steering while better preserving MMLU, BBH, and IFEval performance than SFT, CAA, and neuron-level baselines. On Qwen3-4B, CANS reduces HarmBench attack success from $15.50\%$ to $1.75\%$ with a $1.11$-point decrease in average capability. These results support CANS as a practical approach to behavioral control across domains.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35529

WorldPager: Zero-Stall Tiered Memory for Long-Horizon World Models on a Single Consumer GPU

Bingnan Liu, Ruini Xue

原文摘要

Interactive world models keep the KV of past chunks as retrieval memory so that revisited places stay consistent. On a single consumer machine (one RTX 4090, 32 GiB of host memory, one NVMe SSD), long sessions with such memory hit two walls. The first is capacity: the KV of Matrix-Game 2.0 exhausts host memory after 45 seconds of video in bf16 and after 2.8 minutes in int4. The second is the model's positional horizon of 4.3 minutes. We show that this memory is capacity-bound rather than bandwidth-bound and that its access set is known before compute, because the camera pose is an input. An analytical model built on these observations predicts spill points to within one chunk and classifies zero versus non-zero stalls correctly on held-out configurations. Guided by it, WorldPager stores int4 KV in a GPU/DRAM/NVMe hierarchy instead of recomputing it, prefetches each chunk's memory from the extrapolated pose, never waits on a miss, and writes back in the background; to our knowledge, it is the first to place the KV memory of an interactive world model on an NVMe tier with zero-stall prefetching. With training-free position handling, it generates 37.5 minutes of video continuously with zero stalls and constant host memory, at about 9.5 frames per second. On a sealed test set, int4 storage shows no measurable quality loss; on long-gap revisits it lowers image quality by about one MUSIQ point.

cs.DC · cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.21978

Beyond Reasoning Gains: Mitigating General-Capability Forgetting in Large Reasoning Models

Hoang Phan, Xianjun Yang, Yuanshun Yao, Jingyu Zhang, Shengjie Bi, Xiaocheng Tang, Madian Khabsa, Lijuan Liu 等 9 位作者

原文摘要

Reinforcement learning with verifiable rewards (RLVR) has delivered impressive gains in mathematical and multimodal reasoning and has become a standard post-training paradigm for contemporary language and vision-language models. However, the RLVR recipe introduces a significant risk of capability regression, in which models forget foundational skills after prolonged training without employing regularization strategies. We empirically confirm this concern, observing that open-source reasoning models suffer performance degradation on core capabilities such as perception and faithfulness. While imposing regularization terms like KL divergence can help prevent deviation from the base model, these terms are computed on the current task and therefore do not guarantee preservation of broader knowledge. Meanwhile, commonly used experience replay across heterogeneous domains makes it nontrivial to decide how much training emphasis each objective should receive. To address this, we propose RECAP-a replay strategy with dynamic objective reweighting for general knowledge preservation. Our reweighting mechanism adapts online using short-horizon signals of convergence and instability, shifting the post-training focus away from saturated objectives and toward underperforming or volatile ones. Our method is end-to-end and readily applicable to existing RLVR pipelines without training additional models or heavy tuning. Extensive experiments on benchmarks using Qwen2.5-VL-3B and Qwen2.5-VL-7B demonstrate the effectiveness of our method, which not only preserves general capabilities but also improves reasoning by enabling more flexible trade-offs among in-task rewards.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.25002

Circuit Distillation

Somin Wadhwa, Silvio Amir, Byron C. Wallace

原文摘要

Model distillation typically focuses on behavioral mimicry, where a student model is trained to replicate a teacher's output while treating its internal computations as a black box. In this work we propose an alternative approach: Distilling the underlying computational mechanisms implemented by a teacher model. Specifically, we propose circuit distillation, which introduces an objective to align internal representations between analogous circuit components in teacher and student models. We propose a method to match ``functionally correspondent'' circuit components and introduce a loss reflecting similarities between the representations that these induce. We evaluate circuit distillation on entity tracking and theory of mind (ToM) tasks using models from the Llama3 family. Our results demonstrate that circuit distillation outperforms standard distillation, successfully transferring algorithmic capabilities by adjusting only a small, targeted subset of student model parameters. This work establishes the feasibility of transferring mechanisms, which may in turn allow for efficient distillation of targeted teacher capabilities via interpretable and controllable internal student mechanisms.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35523

Ranking Function Generation with LLMs for Termination

Rongkang Li, Jiaxin Hu, Taolue Chen, Yong Li, Fu Song, Zhilin Wu

原文摘要

Ranking functions are a fundamental technique for proving loop termination, but constructing them manually is challenging and requires substantial expertise. We propose RAINFALL, a neuro-symbolic framework which follows the generate-verify-refine paradigm and tightly integrates LLMs with symbolic verification. RAINFALL formulates ranking function synthesis as a code-completion task by inserting placeholders into the input program and constraining the LLM to fill four predefined slots for each loop. The completed program is then checked by a symbolic verifier. If verification succeeds, a valid ranking function is obtained; otherwise, the verifier's diagnostics are translated into natural-language feedback that guides iterative refinement of the LLM's output. We evaluate RAINFALL on two public benchmark suites comprising 358 terminating C programs. Using MiniMax V2.7, RAINFALL synthesizes verifiable ranking functions for 323 programs (90.22%), outperforming all promising baselines. In particular, it solves 12 more programs than PROTON 2.1, the best-performing baseline and winner of the SV-COMP 2026 Termination category. Furthermore, combining RAINFALL with PROTON 2.1 increases the success rate to 96.65% (346/358 programs). We also conduct ablation study confirming individual contributions of placeholder contracts, few-shot examples, symbolic verification, and diagnostic information feedback. Experiments across LLMs further confirm the effectiveness of RAINFALL. Our results suggest a generic, sound framework for ranking-function synthesis and, beyond termination, LLM-assisted synthesis of formally checkable liveness certificates, both of which represent new directions of software engineering research.

cs.SE · cs.AI · cs.LO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2412.12786

Forbidden Patterns in Mixed Linear Layouts

Deborah Haun, Laura Merker, Sergey Pupyrev

原文摘要

An ordered graph is a graph with a total order over its vertices. A linear layout of an ordered graph is a partition of the edges into sets of either non-crossing edges, called stacks, or non-nesting edges, called queues. The stack (queue) number of an ordered graph is the minimum number of required stacks (queues). Mixed linear layouts combine these layouts by allowing each set of edges to form either a stack or a queue. The minimum number of stacks plus queues is called the mixed page number. It is well known that ordered graphs with small stack number are characterized, up to a function, by the absence of large twists (that is, pairwise crossing edges). Similarly, ordered graphs with small queue number are characterized by the absence of large rainbows (that is, pairwise nesting edges). However, no such characterization via forbidden patterns is known for mixed linear layouts. We address this gap by introducing patterns similar to twists and rainbows, which we call thick patterns; such patterns allow a characterization, again up to a function, of mixed linear layouts of bounded-degree graphs. That is, we show that a family of ordered graphs with bounded maximum degree has bounded mixed page number if and only if the size of the largest thick pattern is bounded. In addition, we investigate an exact characterization of ordered graphs whose mixed page number equals a fixed integer $ k $ via a finite set of forbidden patterns. We show that for every $ k \ge 2 $, there is no such characterization, which supports the nature of our first result.

cs.DM · math.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35522

Generative Structure from Motion with Native 3D Diffusion

Haobin Duan, Binbin Huang, Mochu Xiang, Yiqun Zhao, Zibo Zhao, Yi Ma, Shenghua Gao

原文摘要

Recovering an object's 3D structure and camera motion from a few uncalibrated views is a fundamental problem in computer vision. Classical structure from motion methods recover 3D geometry consistent with the input images but cannot recover unseen regions; native generation methods produce plausible 3D objects which are related but not necessarily faithful to the input images. Methods simply concatenating reconstruction and generation allow error to cascade across the two stages, instead of producing a 3D object consistent with both the distribution and the input. We propose GenSfM, which integrates image-based 3D reconstruction with a native 3D diffusion model. In particular, we use a multi-modal diffusion transformer to jointly denoise the shape latent and per-view pose latents under shared self-attention, grounding generated geometry in the observed views while using object-level priors to infer unobserved regions. A compact 3D-native UV-volume tokenization keeps the joint sequence small as views are added, and a pose-aware stage refines geometry and texture by sampling per-voxel image features at the recovered camera poses. On Toys4k and GSO, for $4$ uncalibrated views, GenSfM improves novel-view PSNR by $3.2$-$4.5$ dB and reduces median Chamfer distance by $2.8$-$3.8\times$ over a strong native-3D generation baseline, while recovering input-view poses over $96\%$ Acc@$5^\circ$ without any external pose-estimation backbone, post-hoc optimization, or known intrinsics. Project page: https://dotb1ue.github.io/GenSfm/.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35521

PRISMA: Differentially Private Synthetic In-Car Telemetry via Differentiable Simulation

Kilian Zeiseweis, Nele Sophie Brügge, Marven Kummerfeldt, Ali Raza, Esfandiar Mohammadi

原文摘要

Differentially private synthesis of in-car energy consumption data is challenging because the output is high-dimensional, and known lower bounds show that the privacy-utility trade-off degrades with the square root of the number of output dimensions. However, we observe that the intrinsic dimensionality of in-car energy consumption is much lower than that of the raw trace: given a small set of contextual personal variables-such as start point, end point, the car model, and the driving behavior-the consumption profile can be largely explained by publicly available map data (including elevation) combined with a physical model of vehicle energy use. We exploit this low intrinsic dimensionality in PRISMA, a Privacy-preserving In-car Synthesis and Modelling Algorithm. PRISMA involves three stages: (i) energy consumption traces are encoded into a low-dimensional latent space; (ii) the distribution of latent vectors is synthesized using the differentially private tabular data synthesis algorithm AIM, and (iii) samples drawn from this distribution are decoded back into full energy consumption traces via a differentiable physics-based simulation, a step that consumes no additional privacy budget since it uses only public information. By using a compact, physically-grounded latent representation of the traces, PRISMA significantly reduces the effective output dimension. Our experimental results show that PRISMA achieves for a competitive $\varepsilon = 0.5$ a better data synthesis performance than prior tabular and marginal-based DP synthesis baselines by preserving the diversity of the original power consumption profiles without collapsing to a small set of modes.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35518

S3AC: Stabilized, Selective, and Scalable Actor-Critic for LLM Reasoning

Shuo Shen, Guangran Cheng, Chengqi Lyu, Xiangsen Wang, Yanan Sun, Wenwei Zhang

原文摘要

Critic-free methods are widely used in reinforcement learning with verifiable rewards (RLVR) for long chain-of-thought (CoT) reasoning, avoiding difficult and costly critic training. Common alternatives lack token-level credit assignment and typically rely on grouped rollouts to estimate prompt-specific baselines. A critic can address both limitations, but long-CoT critic training is hindered by insufficient coverage of trajectories with diverse lengths and early gradients dominated by the newly initialized value head. We introduce S3AC (Stabilized, Selective, and Scalable Actor-Critic) for LLM reasoning, built around a practical critic-training recipe. The trained critic provides token-specific advantages and prompt-specific baselines. Using the prompt-level baseline to estimate difficulty, we propose Difficulty-Aware Prompt Sampling (DAPS). DAPS combines this estimate with outcome history to select prompts before generation, reducing rollout waste without offline difficulty initialization. S3AC improves group-rollout performance across dense and mixture-of-experts models from 1.5B to 397B parameters, outperforming a modern GRPO variant (GRPO-X). At 35B, it sustains single-rollout training while tested critic-free baselines deteriorate after initial gains.

cs.LG · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35516

Learning High-Quality Pseudo-Labels for Point-Supervised Temporal Action Localization

Zhenying Fang

原文摘要

Point-supervised temporal action localization (PTAL) aims to recognize and localize action instances in untrimmed videos using only point-level annotations. Existing pseudo-label-based methods have made substantial progress in PTAL. However, confidence-based initialization can overlook well-localized proposals. Meanwhile, existing methods either keep pseudo-labels fixed during detector training or update them without fully exploiting alternative boundary evidence from the original proposal pool. These limitations constrain supervision quality, thereby hindering detection performance. To learn high-quality pseudo-labels, we propose a framework that integrates prior-guided initialization with consensus-guided refinement during detector training. Our framework comprises two modules: Prior-Guided Initialization (PGI) and Consensus-Guided Refinement (CGR). PGI combines proposal confidence with point-derived priors to establish instance-specific reliability for initial pseudo-label selection and candidate retention. CGR continually uses the retained original candidates to construct boundary targets, combining their consensus with evolving detector predictions to refine pseudo-labels throughout training. Experiments on four benchmarks demonstrate that our method achieves competitive detection performance compared with existing methods. In particular, our method achieves 68.5\% average mAP over tIoU thresholds 0.1--0.7 on THUMOS14, surpassing PseR by 3.7 percentage points. Code is available at https://github.com/zhenyingfang/LHQP

cs.CV

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新论文 · v1arXiv:2610.35515

HiFiPix: Bringing High-Fidelity Real-World Super-Resolution Back to Pixel Space

Wenjie Li, Du Chen, Hui Wang, Linlong Fan, Youwei Lv, Zeyi Chen, Zehan Chen, Jinwei Chen 等 11 位作者

原文摘要

Diffusion-based real-world image super-resolution suffers from limited reconstruction fidelity due to information loss in VAE-based latent representations. Recently, pixel-space diffusion models offer a promising high-faithfulness alternative by avoiding latent compression, but adapting them to Real-ISR remains challenging due to inherent patch boundary artifacts and degraded generative capability. To address the issues and maximize the potential of pretrained pixel-space diffusion models, we propose HiFiPix, a pixel-space Real-ISR framework capable of ultra high-fidelity reconstruction and fine-grained visual generation effects. To suppress patch boundary artifacts, we penalize unrealistic high-frequency periodic components with a Cross-Scale Wavelet Discriminator (CWSD) for image-level supervision, and Mixup Contrastive Self-Distillation (MixCSD) for intermediate representation regularization. Furthermore, we uncover a heterogeneous generation-fidelity trade-off across low-rank adapters (LoRA) and propose Selective LoRA Pruning (LoRA-SP) to adaptively prune LoRA layers, enhancing generative capability with minimal deterioration of reconstruction fidelity. Extensive experiments demonstrate that HiFiPix consistently outperforms existing state-of-the-art latent space methods in terms of both restoration accuracy and perceptual quality by a large margin. Code and models will be released.

cs.CV

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新论文 · v1arXiv:2610.35514

Depth Improves Training-Time Scaling for Difficult Tasks in Deep Linear Networks

Taishi Watanabe, Ryo Karakida, Jun-nosuke Teramae

原文摘要

Neural scaling laws describe the predictable power-law dependence of model performance on training time, dataset size, and model scale, with the exponents quantifying how performance changes with resources. Recent theory has shown how scaling exponents are shaped by the data distribution and the structure of the learning task, but whether network depth can improve training-time scaling has remained unsettled. Through a refined analysis of deep linear networks, we clarify that the task spectrum determines when increasing network depth improves training-time scaling. For spectrally difficult tasks, increasing depth improves the asymptotic training-time scaling exponent, whereas for easy tasks, the exponent remains independent of depth, with depth affecting only a logarithmic correction at the boundary between these regimes. Remarkably, we find that a reparameterization of time governed by the predictor norm provides a unified description of the dynamics across task difficulties. Under this reparameterization, the loss follows a common scaling law whose exponent is independent of depth. Numerical experiments on a task derived from benchmark image data and with nonlinear networks exhibit qualitatively consistent task-dependent effects of depth on training-time scaling. Our findings advance the theoretical understanding of how task structure shapes the effects of network architecture on learning and offer a basis for studying scaling behavior across broader classes of neural networks.

stat.ML · cs.LG

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新论文 · v1arXiv:2610.35512

MATCHA: A Chinese Multi-Disorder Mental Health Dataset with Cross-Lingual Analyses on Social Media

Bingrui Jin, Baihan Li, Zitong Li, Kunyao Lan, Shimeng Li, Kenny Q. Zhu, Mengyue Wu

原文摘要

Mental health research on social media has been dominated by English-language datasets and DSM-5-centered resources, limiting cross-lingual and cross-cultural understanding of online mental health expression. We introduce MATCHA, the first Chinese multi-disorder mental health dataset that links disorder-specific Weibo Super-Topic communities with users' broader public timelines. MATCHA covers seven DSM-5 disorders and a control group, comprising 46,025 cohort-level users and 1.8 million posts. The dataset distinguishes users with explicit self-disclosure evidence from broader disorder-related participants through an LLM-assisted annotation pipeline, and is released in a rigorously de-identified, controlled-access format. Using MATCHA, we analyze behavioral, linguistic, symptom-related, and life-event-related patterns across disorders, and conduct cross-lingual comparisons with Twitter-STMHD. Our analyses show that Weibo Super-Topics function as concentrated spaces for affective and health-related expression, while cross-platform differences between Weibo and Twitter often exceed cross-disorder variation. MATCHA provides a new resource for community-aware and cross-lingual mental health NLP research.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.06795

LoRAScan: Detecting Backdoor Prompts in Low-Rank Adapters for Large Language Models via Down-Projection Activation Spikes

Doniyorkhon Obidov, Honggang Yu, Xiaolong Guo, Kaichen Yang

原文摘要

Low-rank adaptation (LoRA) enables efficient specialization and distribution of large language models through compact adapters. However, untrusted adapters introduce a supply-chain threat: a backdoored adapter can cause a model to generate harmful content, malicious code, political propaganda, or covert advertisements when an input contains a hidden trigger. Adapter-agnostic defenses merge the adapter with the base model, which dilutes backdoor signals and reduces detection performance. Existing adapter-aware methods do not address how to safely use a potentially backdoored adapter. Instead, they either train a defensive adapter to repair a backdoored base model, addressing the inverse problem rather than securing the adapter itself, or rely on a classifier that flags the entire adapter as suspicious and requires separate mitigation. These methods overlook the distinct latent-space signatures produced by trigger-bearing inputs in backdoored adapters. We introduce LoRAScan, the first adapter-aware defense that detects and rejects trigger-bearing inputs at inference time without modifying adapter parameters. Our key observation is that a small subset of LoRA insertion sites, approximately 5%, remains stable across clean inputs but exhibits highly concentrated spikes in LoRA down-projection activations when a trigger is present. LoRAScan identifies these low-variance insertion sites before model deployment and monitors them during inference. Across standard LLM backdoor benchmarks, LoRAScan rejects 98.49% of malicious inputs with a small error rate on clean inputs, outperforming existing defenses across diverse evaluation settings.

cs.CR · cs.AI · cs.CL

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新论文 · v1arXiv:2610.35509

Governing Enterprise AI Traffic at Scale: Design and Operation of a Policy-Driven, Multi-Vendor LLM Gateway on Envoy

Mithun Sasidharan, Srinivasan Manoharan, Rupesh Thakur, Abhinav TK, Ashwin Hegde, Anand Gautam

原文摘要

Large organizations consume large language models (LLMs) from several vendors at once, each with its own API dialect, credential model, streaming protocol and pricing. Integrated directly, this heterogeneity yields credential sprawl, format lock-in, unbounded spend and inconsistent governance. We present the design and production operation of the Cosmos LLM Gateway, the governed entry point for enterprise LLM traffic at PayPal. Built on Envoy, it splits work by latency profile: I/O-bound control decisions (authentication, credential brokering, policy and budget evaluation) run in an out-of-process external-processing service, while wire-format translation, token metering and prompt-cache shaping run in-proxy as a Rust WebAssembly filter, so that, by default, streamed response body chunks never wait on an out-of-process call. Models, providers and prices are largely validated data rather than code. Governance is declarative policy evaluated by Open Policy Agent and managed through a control plane with role-scoped authoring, approvals, pilots, shadow-mode rollout, usage notifications and versioned policy bundles. For use cases that require it, a data-loss-prevention layer can block, redact or pseudonymize detected sensitive content before it reaches a provider. Spend scopes are views over per-model usage counters, so a new budget applies to spend already recorded in its window without migration. As of September 2026, the gateway serves more than 12,000 developers and hundreds of business use cases, handling roughly 200 billion tokens per day across more than 100 models at 99.999% availability to business. In a benchmark of the core request path against a synthetic upstream, it adds a median of 4 ms and a p99 of 11 ms of overhead at more than 2,200 requests per second on two 4-vCPU instances. We report the architecture, policy model, evaluation and lessons learned.

cs.DC · cs.AI · cs.SE

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版本更新 · v2arXiv:2608.00283

On the Complexity of Computing Outputs of a Metric Turing Machine

Yaroslav Ivanashev

原文摘要

The classes MidP, MedP, and $\small{\overline{\text{MedP}}}$ contain functions that compute the median solution for certain types of problems. In this paper, for these classes we introduce analogous classes of functions that compute the k-th solution, where k is an order function that depends on the input. We prove that the classes MidP, MedP, and $\small{\overline{\text{MedP}}}$ are polynomial-time 1-Turing inter-reducible with the corresponding classes, where the order function is from FP or FP$^{\text{#P}}$. For MedP we also prove that it coincides with the corresponding classes, where the order function is from FP or #P. For several inclusions between function classes we give equivalent inclusions between language classes. In particular, we establish inclusion relations between MaxP and median classes MidP, MedP, and $\small{\overline{\text{MedP}}}$. We also prove that NPSV$_{\text{t}} \subseteq$ MaxP $\subseteq$ FP$^{\text{NP}}$ and both inclusions are proper if and only if NP $\neq$ coNP.

cs.CC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35505

Constraint Learning by Optimizing over Trained Neural Networks: A Pragmatic Tutorial

Jiaxiao Fang, Thiago Serra

原文摘要

Constraint learning is an emerging paradigm of modeling optimization problems with the help of machine learning. In this paradigm, trained machine learning models are embedded into the optimization model. For example, we can learn constraints empirically from available data, or we can avoid using a nonlinear objective function by learning a simpler approximation for it. Neural networks become a compelling option if the machine learning models must capture more elaborate and nuanced relationships. However, optimization models embedding neural networks may become too large or too slow to solve. In this tutorial, we present practical considerations for an effective and efficient use of constraint learning with neural networks and validate it with a case study highlighting the benefit of smaller and sparser networks.

math.OC · cs.LG

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版本更新 · v3arXiv:2511.22693

Generative Anchored Fields: Endpoint Learning for Generative Modeling

Deressa Wodajo Deressa, Hannes Mareen, Peter Lambert, Glenn Van Wallendael

原文摘要

We present Generative Anchored Fields (GAF), a generative model that learns independent endpoint predictors, \(J\) (noise) and \(K\) (data), from any point on a linear bridge. Instead of learning the local path along a trajectory, as in diffusion or flow-based models, GAF is trained to identify the endpoint coordinates of a bridge state directly via an endpoint regression objective. Central to our approach is the decoupling of time-dependent feature learning from endpoint prediction. Unlike the transport-based models that learn a monolithic time-conditioned predictor that maps noise to data via a unified field, GAF uses a shared trunk, \(Φ\), which produces a time-conditioned feature bank and a pair of linear predictors, \(J\) and \(K\). This separation of feature learning from prediction enables \textit{Transport Algebra}: algebraic operations on multiple \(J\) and \(K\) heads for compositional sampling. Additionally, with class-specific \(K_n\) heads, where \(n\in\{1,...,N\}\) for \(N\) classes, GAF defines directed transport maps between a shared base noise distribution and multiple data domains, allowing controllable interpolation, multi-class composition, and semantic editing. This is achieved either directly on the predicted data coordinates (\(K\)) using (i) Iterative Endpoint Refinement (IER), a novel sampler that achieves high-quality generation in \(5-8\) forward steps, or (ii) on the derived velocity field \(v=K-J\). GAF achieves strong sample quality with an FID of 2.25 on ImageNet \(256\times256\) while treating compositional generation as an architectural primitive. Code available at https://github.com/IDLabMedia/GAF

cs.LG

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新论文 · v1arXiv:2610.35504

Model reduction in time: Learning flow maps for reducing stochastic dynamical systems in time

Shreya Jha, Benjamin Peherstorfer

原文摘要

Predictions with dynamical systems can be computationally expensive---even with reduced models---because reaching future prediction times can require many sequential time integration steps. We propose Flow-Map First-Order Trajectory Matching (FM-FTM), a nonintrusive model reduction approach that accelerates ensemble predictions of stochastic dynamical systems by learning flow maps that replace multiple time-integration steps. Our method uses first-order trajectory matching (FTM) to learn a deterministic representation of the stochastic probability-current dynamics and then reduces its evolution over time into a flow map whose evaluation directly yields predictions over long time horizons. Numerical experiments on evolution problems with stochastic forcing demonstrate that FM-FTM accelerates ensemble predictions by up to two orders of magnitude while accurately predicting time marginals and ensemble statistics.

cs.LG · math.NA

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