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版本更新 · v2arXiv:2603.13018

Support is Search

Alexander V. Gheorghiu

原文摘要

For intuitionistic propositional logic, Sandqvist's base-extension semantics defines a support relation parametrised by atomic bases, with validity identified as support in every base. Sandqvist's completeness theorem answers the global question: which formulae are valid? This paper addresses the local question: given a fixed base, what does support in that base correspond to? We show that support in a fixed base coincides with proof-search in a hereditary Harrop logic program with a quantifier over atoms, via an encoding of formulae as logic programming goals. The encoding is a variant of the Russell-Prawitz translation of the connectives, with quantification restricted to atoms, and it is obtained by reading the semantic clauses in continuation-passing style. So read, the universal quantifiers over base extensions and atoms in those clauses need not range over a completed totality: they are eigenvariables under a standard freshness discipline. Base-extension semantics thereby acquires an explicit computational content: support is proof-search. The result complements Sandqvist's global theorem with a local correspondence and makes the constructive character of the framework explicit.

cs.LO · math.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16524

A Joint LLM and Optimization Agent Solution for Drone Scheduling in Victim Search Missions

Yinghao Zhang, Yalin Liu, Hong-Ning Dai, Yan Zhang, Qubeijian Wang, Bishenghui Tao, Yaohan Li

原文摘要

Victim search in wilderness areas is time-critical. Drones enable rapid area search, but scheduling them is challenging because victims may continue moving, detection results are uncertain, and effective decisions must combine structured mission information with unstructured contextual clues and domain knowledge. To address this, we propose an agent-aided drone scheduling system. The system employs an agent at the ground control station (GCS) to coordinate multiple drones over successive search cycles. In each cycle, the agent applies a scheduling policy that combines a large language model (LLM) for reasoning about where to search with an optimizer for determining how to dispatch the drones under energy constraints. Simulation results show that the proposed system consistently completes the victim-search mission, whereas none of the six non-LLM baselines does so and direct LLM scheduling is considerably less reliable. The source code is publicly available at https://github.com/1643204431/llm-uav-victim-search.

eess.SY · cs.AI · cs.ET

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16523

RESYNC: Residual Latent-State Synchronization for Deadline-Bound Streaming Inference

Eljas Linna, Hao Dong, Juho Kanniainen

原文摘要

Predicting every observation in a high-rate stream requires a model to finish before the next arrival. A large model may offer better predictions but miss that deadline. We propose RESYNC: the large model runs asynchronously, while a fast path updates its latest exact but stale latent state using the observations received since that state was computed and applies its unchanged prediction head. The updater learns from the frozen model's representations without task labels. Across one real and two synthetic streams and five architecture families, RESYNC beats the best compact or distilled alternative with a paired 95% interval above zero in all twelve dataset--architecture pairs with a material teacher advantage at anchor ages of 4--12 observations, and in eleven pairs at older tested ages up to 32. The share of the teacher's gain preserved remains complete on the real stream but is lower and generally declines with anchor age on the synthetic streams. In live replay with 1 ms arrivals, the synchronous large models cannot deliver any predictions on time while RESYNC serves at least 99.4% of arrivals within the interval, with 0.35--0.62 ms median latency on one CPU thread: around the same speed as or faster than the compact model, and 7.9--17.7$\times$ faster than synchronous large-model inference. RESYNC targets settings with background compute available but a strict per-observation response deadline.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16522

PISim: A Unified Photonic Inference Simulator for Hardware-Aware Cross-Layer Design-Space Exploration

Andreas Falkenberg

原文摘要

Photonic neural accelerators offer ultra-low latency linear algebra, yet their usable numerical precision, sustainable throughput, and application accuracy remain tightly bounded by surrounding electro-optical interfaces and physical non-idealities. Pre-fabrication design-space exploration (DSE) requires a cross-layer electronic design automation (EDA) framework capable of evaluating disparate optical topologies under identical neural workloads and physical assumptions. We present \textbf{PISim}, a unified Photonic Inference Simulator that couples pretrained deep neural network execution with physics-based models of six accelerator architecture families, six electro-optic modulator technologies, four optical source organizations, and direct or coherent readout schemes. PISim embeds stage-resolved impairment operators---covering control and analog-to-digital quantization, finite extinction ratio, shot noise, correlated relative-intensity noise (RIN), thermal drift, propagation loss, and channel crosstalk---alongside an analytical cycle-level interface timing model to expose electronic-optical bottlenecks. Using simulated MNIST multilayer perceptron (MLP) benchmarks, we demonstrate how PISim identifies optimal precision operating points under multi-impairment stress (peaking at 6 bits for Mach--Zehnder and microring modulators), quantifies a 52.5 percentage-point accuracy gap between VCSEL-array and parallel-fanout laser architectures arising, under the modeled conditions, from power partitioning and correlated source noise, and models interface-limited throughput scaling across emitter-array widths from 32 to 128 channels. This comprehensive framework establishes an extensible, reproducible foundation for accuracy-constrained Pareto optimization prior to photonic tape-out.

physics.optics · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16521

Online Survivable Network Design with Arbitrarily Small Connectivity Loss

MohammadTaghi Hajiaghayi, Saeed Seddighin

原文摘要

In the online survivable network design problem, a graph with edge costs is known in advance, and requests arrive one at a time. Each request is a pair of vertices $(s,t)$ with a connectivity requirement $k$, and the algorithm must irrevocably buy edges so that $s$ and $t$ are joined by $k$ edge-disjoint paths. The goal is to keep the total cost close to that of an optimal offline solution. Gupta, Krishnaswamy, and Ravi~\cite{GKR12} obtain an $O(k\log^3 n)$-competitive algorithm, where $k$ is the maximum requirement, without relaxing the connectivity requirement. Dehghani, Ehsani, Hajiaghayi, Liaghat, and Seddighin~\cite{DEHLS18} obtain a polylogarithmic competitive ratio when the connectivity requirement is allowed to be relaxed by a factor of $2+\varepsilon$. We show that relaxing the connectivity requirement by a factor of only $1+\varepsilon$ suffices to obtain a polylogarithmic competitive ratio.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16517

UniSpark: Exploring Hybrid Attention for Multimodal Understanding and Generation

Jiancheng Pan, Yuzheng Liu, Hengyuan Cao, Qiyuan Zhang, Yangyan Li, Biao Gong, Zheng Zhang, Ka Long Cheng 等 10 位作者

原文摘要

We present UniSpark-9B, a unified multimodal model that investigates hybrid attention for visual understanding and generation. Built on a Mixture-of-Transformer-Experts backbone with approximately 9B parameters in each branch, UniSpark combines full attention for explicit token interactions with GatedDeltaNet linear attention for compact context propagation. Autoregressive text prediction and visual flow matching jointly support visual question answering, text-to-image generation, instruction-guided editing, and interleaved text-image prediction. In interleaved generation, noisy visual targets are isolated from subsequent predictions, while clean visual representations provide context for later outputs. A blockwise protocol enforces this distinction through attention masks in full-attention layers and recurrent-state retention rules in linear-attention layers. The selected training sources contain 159.7M generation samples, 213.4M understanding samples, and 624.8K interleaved sequences. A four-stage curriculum jointly trains multimodal understanding and visual generation, refining answer accuracy and instruction following while extending image synthesis to 2K. UniSpark scores 2470.3 on MME-S and 0.83 on GenEval. Qualitative examples further illustrate its capabilities in interleaved image generation, video synthesis, and video editing. Together, these results demonstrate that hybrid attention provides an effective backbone for unified multimodal understanding and generation.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16515

PiLLVD: A Real-World Low-Light Video Dataset for Dynamic Scenes Captured with Off-the-Shelf Cameras

Yaser Abu Awwad, Paul L. Rosin, Oktay Karakuş

原文摘要

Low-light image and video enhancement has received increasing attention in recent years. However, most existing enhancement methods are developed and evaluated on still images, while progress in low-light video enhancement remains limited by the lack of real-world paired video datasets. Acquiring paired low-light and normal-light video sequences is challenging because it requires spatially and temporally aligned captures while avoiding motion blur, exposure mismatch, and correspondence errors in dynamic scenes. To address this gap, we introduce PiLLVD, the Pi-based Low-Light Video Dataset, a paired low-light video dataset captured using an accessible Raspberry Pi-based multi-camera system with off-the-shelf cameras. PiLLVD provides paired low-light and aligned normal-light reference sequences for dynamic indoor and outdoor scenes, including diverse human and object motion under varying illumination conditions. Unlike existing datasets that are often image-based, static, synthetic, or limited to simple motion patterns, PiLLVD focuses on motion-rich scenes where frame-to-frame stability and motion-related artefacts are important for evaluation. In addition to presenting the dataset and acquisition pipeline, we benchmark representative pre-trained low-light video enhancement methods on PiLLVD under a zero-shot setting. The results show that existing methods do not generalise uniformly across the proposed sequences, highlighting the challenges posed by the proposed dataset and its value for both benchmarking existing methods and supporting the development of future low-light video enhancement algorithms.

cs.CV · eess.IV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16514

Learning Temporal Dynamics with Derivative Representations for Time Series Forecasting

Shujie Duan, Jia Hong Puah, Haocheng Wu, Yifei Mao, Hongting Zhou, Chenxuan Li, Zitao Fang, Yang Lu 等 9 位作者

原文摘要

Time series forecasting aims to predict the future evolution of complex systems by learning their underlying dynamics from past observations. Despite recent advances in deep learning, Transformers in particular still struggle to capture the temporal evolution of system dynamics. Existing approaches have been largely driven by architectural innovation, with comparatively little attention paid to training strategies that explicitly exploit the temporal structure governing system dynamics. In this work, we introduce DeltaTSF, a dynamics-aware forecasting framework based on discrete derivative representations of time series. Specifically, DeltaTSF jointly learns the series and their discrete temporal derivatives across both input and forecast horizons within a unified, model-agnostic framework. By coupling these representations over time, the model is encouraged to maintain temporal consistency, a property we show to induce a form of discrete Taylor consistency during training. This formulation enables the model to better reflect the underlying system dynamics, resulting in more stable and physically coherent forecasts. Notably, DeltaTSF does not require explicit derivative computation and introduces no additional computational overhead at inference time. Extensive experiments across diverse benchmarks and backbone architectures demonstrate that DeltaTSF consistently improves forecasting performance and yields more dynamically coherent predictions than strong SOTA methods. These results highlight the effectiveness of enforcing dynamic coherence through derivative representations as a model-agnostic paradigm that operates alongside and complements architectural design. Source code will be made publicly available upon acceptance.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.13636

An Interpretable Stylometric Framework for Early Chinese Buddhist Translator Styles: A Case Study on the Attribution of Disputed Works of An Shigao

Yating Pan, Xinyue Zhang, Jun Wang

原文摘要

Attributing early Chinese Buddhist translations requires evidence that scholars can trace back to the texts. We develop an interpretable stylometric framework that uses features defined or reviewed by Buddhist philologists, including function characters, sentence formulas, sentence-final characters, sentence length, and part-of-speech n-grams. We construct stylistic profiles from 31 authenticated translations by seven translators from the Eastern Han through the Western Jin, then compare eight disputed texts with An Shigao's profile. Two texts, T1557 and T0101, show the strongest stylistic similarity to the authenticated An Shigao corpus, with scores of 0.783 and 0.687, respectively. The attributions agree with those reported by a published BERT-based approach on the same set of disputed texts. The framework requires no task-specific training and allows scholars to inspect which linguistic features support each proposed attribution.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16507

Input-output characterization of robotic grasping: Connecting design parameters to grasp behavior through the admittance

Julien Vanderheyden, Guillaume Drion, Pierre Sacré, Fulvio Forni

原文摘要

Low-impedance grippers make the dynamics of a grasp matter. Current grasp metrics, however, are static: they describe how a held object resists constant loads, not how it responds to time-varying ones. To quantify this, we rely on small-signal analysis to characterize a grasp by its admittance, the transfer matrix relating load perturbations to object displacements around an equilibrium. This transfer matrix recovers the stiffness and stability of the grasp at zero frequency and captures its natural frequencies and peak gain under time-varying loads. We apply this characterization to a planar two-finger gripper under proportional-derivative control. We show that two grasps of the same object in the same position and with the same contact forces can differ in stiffness by two orders of magnitude. Likewise, two grasps that share the same stiffness can exhibit very different resonances. Nonlinear simulations confirm the effectiveness of our small-signal analysis in capturing the dynamic behavior of grasping.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2508.17349

2-Layer Fan-Planarity in Polynomial Time

Yasuaki Kobayashi, Yuto Okada

原文摘要

In this paper, we give a polynomial-time algorithm for deciding whether a bipartite graph admits a 2-layer fan-planar drawing. This resolves an open problem posed in several papers since 2015. We also obtain a linear-time algorithm for the one-sided case.

cs.CG · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16502

SignRot: LLM Quantization with Massive Outlier-Aware Sign-Adjusted Rotation

Jaehun Gim, Jaewoo Kim, Gyuwan Kim, Seo Yeon Park

原文摘要

While quantization compresses LLMs and accelerates inference, outlier activations impede efficient low-bit representation. In particular, massive outliers cause substantial performance degradation. Although rotation-based methods that rely on randomized Hadamard transforms for online inference aim to mitigate this, they can produce fragmented distributions with empty bins when a massive outlier dominates a token's activation. This fragmentation severely undermines uniform quantization performance. Moreover, massive outliers concentrate in the down-projection input, where the preceding nonlinearity requires the rotation to be applied at inference time. This constraint prevents training-based approaches such as SpinQuant from learning the online rotation at this position, as a learned dense matrix cannot use the Fast Walsh-Hadamard Transform and would require a full matrix multiplication during inference. To address this, we propose SignRot (Sign-adjusted Rotation), which identifies massive outlier channels and constructs a sign adjustment vector that unifies the signs of the corresponding Hadamard row, collapsing the bimodal distribution into a unimodal profile amenable to quantization. Since the aligned outlier shifts the activation mean away from zero, we pair SignRot with asymmetric activation quantization, whose zero-point absorbs this shift. As a lightweight, drop-in improvement compatible with any Hadamard-based quantization method, SignRot requires only a single calibration forward pass and adds only element-wise sign flips at inference, without an additional matrix multiplication. Applying SignRot to QuaRot achieves a WikiText2 perplexity of 5.88 and an average zero-shot reasoning accuracy of 72.73, surpassing SpinQuant on the LLaMA3-70B model. Code is available at https://github.com/HYU-ARK-Lab/SignRot.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16500

EvenVLM: A Text and Multimodal Dataset for the Even Language with Vision-Language Fine-Tuning

Daniil Makovetskii, Ivan Bondarenko

原文摘要

We present EvenVLM, the first text and multimodal (VQA) dataset for Even (Lamut), an endangered Northern Tungusic language spoken by approximately 6,800 people in Siberia. Our dataset comprises 50,521 structured Even-Russian entries extracted from dictionary and phrasebook sources via GPT-4o Mini-based parsing, from which we synthetically generate 107,543 training examples spanning translation, grammar, reading comprehension, and semantic classification. To address tokenization inefficiency on Even's agglutinative morphology, we propose a three-step tokenizer expansion algorithm that adds Even-specific graphemes (eng, oe, o-umlaut), whitelisted vocabulary, and BPE merges, reducing average tokens per word by 27% (4.99 to 3.64). Using LoRA fine-tuning of Qwen3-VL-4B, we adapt the model for text tasks (0.53% trainable parameters, rank 32) and separately for visual question answering (rank 64, frozen vision encoder). Our best configuration achieves 11.4% exact match and a 13.6 weighted score on text benchmarks, 0.1283 chrF for sentence translation, and 65% accuracy on VQA (up from 0% for the base model and text-only fine-tuning). We release our datasets, evaluation benchmarks, and training pipeline to support further research on Even and other low-resource languages.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16499

Recalibrating a Typed-Decision Model in an Afternoon: A Hands-On Recipe for Laya's Confidence Scores

Mohammed Nayeem Khan, Aliyas Ahmad Misri

原文摘要

A typed-decision model such as Laya reads a text and a list of options that the caller defines, for example ticket categories, and returns a probability for each option. Code around it compares the top probability with a threshold to answer, abstain or escalate to a person. Earlier work reports, for a Turkish model, that temperature scaling can raise held-out calibration error (ECE). This tutorial gives a five-step recipe (measure, diagnose, fit, validate, deploy) in 130 lines of numpy with standard methods, none new. On 1,065 synthetic, LLM-labelled items the released Laya checkpoint has a pooled ECE of 0.073 but a bucket-macro ECE of 0.143 (0.127-0.184), the mean over buckets (questions grouped by type and option count): the 12-option bucket is over-confident by +0.22, the three-to-five-option bucket under-confident by 0.05, and they cancel. One global temperature makes pooled ECE worse (0.173); a cross-fitted temperature per bucket brings macro-ECE to 0.073 (0.078 over 10 fold assignments) and reaches 0.089 with 10 labelled items per bucket, but holding out one of seven evaluation sets gives 0.099. After the refit, a gate aimed at 10% error answers 46.3% of items (10.5% wrong) against 6.9% on the published confidences, but one cut-off per bucket on the published confidences answers 45.4%, so most of the gain comes from separating buckets. Confidence-only maps (Platt, histogram, isotonic) need 20 or more labelled items per bucket to approach the temperature; with 320 per bucket the lowest macro-ECE belongs to Platt. In these files the retrained checkpoints store no per-bucket table and the stored 12-option value 0.1006 is clamped to 0.5 at runtime; the refit helps 31 of 33 checkpoints on random folds and 21 with a set held out. Fit and test share one generator and LLM labels, so these are findings about these checkpoints and this data.

cs.AI · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16493

ReA-Bench: Benchmarking Responsible Agent Auditing

Zipeng Ye, Shenyu Dai, Yong Xie, Kun Wang, Jiyang Guan, Zhikai Chen, Jiaqi Weng, Jialing Tao 等 9 位作者

原文摘要

As agents perform tasks autonomously in local and cloud environments, auditing their execution trajectories can help service providers identify risks and improve agent harnesses and foundation models. Our analysis of production and red-team trajectories shows that some risks are repeatedly missed across auditing configurations. Overall recall obscures which risks are missed and whether higher recall comes with more false positives on legitimate behavior. Existing benchmarks offer limited insight into these failures. Generated trajectories can contain inconsistent tool calls and responses, conspicuous risky actions, or repetitive attack patterns. Grouping results by risk type also makes it difficult to tell whether auditors overlooked evidence, accepted an unsupported conclusion, or lacked information needed to make a judgment. We introduce ReA-Bench, a diagnostic benchmark for auditing responsible agent behavior. It organizes auditing challenges into six tiers: internal safety knowledge, contradictions within trajectories, evidence retrieval in long contexts, verification of sources and reasoning, indirect steering, and missing information. It includes challenging benign cases in which actions resemble risky ones but are consistent with the task and its authorization. It also provides paired trajectory versions to test whether auditors detect the same risks more reliably when the trajectory is shortened or missing information is supplied. Most samples are drawn from real production trajectories, with scenarios manually selected and adapted to preserve realistic tasks and risks that could plausibly arise during execution.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16485

Contain, Conform, Compare (Extended Version): A Red-Teamed Evaluation Kit for Small Coding Agents and the Full Record of Its Experiments

Nguyen Minh Thang

原文摘要

A hosted score in the Gemma 4 Developer Agent competition is one noisy number, so development needs a local evaluator that is fast, safe for agent-written code, and matching the hosted scorer's prompts. drydock-eval evaluates a small coding agent (Gemma 4 E4B) at about 250 runs per hour on one RTX 5090. Contain runs each job in a fresh container without network and passed 200 scripted escape tests; conform provides eight checks that compare a local stack's requests with the scorer's configuration, down to rendered tokens; compare provides paired statistics that respect repository clusters. AI agents built and attacked the kit; a human owner approved machine use and release. This extended version repeats the short paper and adds the full record of the experiments made with and around the kit: 9,660 rows of agent runs, 2,494 grading controls and 72 serving measurements, all released. On four L4 GPUs the 31B model decodes 38.1 tokens per second in one stream and E4B 105.8. Six configurations of a 31B agent solved between 37 and 44 of 70 development tasks, and no pairwise difference was detected. A pre-registered five-arm experiment (231 tasks, 4,620 jobs) measured four prompt divergences: no E4B solve-rate change was detected (largest estimate -2.1 points, 95% interval -4.4 to +0.4). Three changes of the E4B agent led their screens by +1.0 to +6.2 points; each was then tested once on new tasks under a frozen pass rule, and none was confirmed (gains -0.35, -0.39 and +4.10 points). The unchanged baseline, run again on the same 128 tasks, moved by 1.17 points, and 65% of these tasks were solved in none of eight runs. Prompts and agent bundles are withheld; every run-level result is public.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2412.16175

Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study

Yilie Huang, Yanwei Jia, Xun Yu Zhou

原文摘要

We study continuous-time mean--variance portfolio selection in markets where stock prices are diffusion processes driven by observable factors that are also diffusion processes, yet the coefficients of these processes are unknown. Based on the recently developed reinforcement learning (RL) theory for diffusion processes, we present a general data-driven RL approach that learns the pre-committed investment strategy directly without attempting to learn or estimate the market coefficients. For multi-stock Black--Scholes markets without factors, we further devise an algorithm and prove its performance guarantee by deriving a sublinear regret bound in terms of the Sharpe ratio. We then carry out an extensive empirical study implementing this algorithm to compare its performance and trading characteristics, evaluated under a host of common metrics, with a large number of widely employed portfolio allocation strategies on S\&P 500 constituents. The results demonstrate that the proposed continuous-time RL strategy is consistently among the best, especially in a volatile bear market, and decisively outperforms the model-based continuous-time counterparts by significant margins.

q-fin.PM · cs.LG · eess.SY · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16484

TAPES: Terminal Adjoint-Propagated Error Scheduling for Financial Diffusion Samplers

Yilie Huang

原文摘要

Financial diffusion samplers are often reused with a small number of function evaluations (NFE) inside scenario and risk pipelines, where numerical time-grid error can distort terminal quantities such as covariances, tails, and drawdown proxies. We study offline grid construction for a frozen probability-flow ODE: the score network, conditioning, Euler solver, report normalization, and deployment NFE remain fixed. TAPES (Terminal Adjoint-Propagated Error Scheduling) estimates signed local defects by Richardson comparisons, transports them through the remaining discrete flow and a terminal-report adjoint, and optimizes the squared norm of their sum before a capped direct refinement. We show that this transported signed sum is the first-order terminal-report error and quantify, under an aligned-defect model, the inefficiency of diagonal criteria. Across six budgets on a regime-switching simulation and chronologically held-out Mag7 contexts, TAPES gives the lowest design- and audit-report errors in all 24 dataset--budget--report rows within the reported five-pipeline panel. Cross-budget mean relative audit-error reductions are about 10--11% on Regime-SV and about 1% on Mag7, with Bonferroni intervals excluding zero; all displayed winners persist under a doubled-resolution reference, and a small Regime-SV Heun probe preserves the TAPES-first audit ordering. TAPES changes only the offline schedule and retains the prescribed deployment NFE; claims concern numerical fidelity to frozen generators.

q-fin.CP · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2601.18681

ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Scheduling

Yilie Huang, Wenpin Tang, Xun Yu Zhou

原文摘要

We consider time discretization for score-based diffusion models to generate samples from a learned reverse-time dynamic on a finite grid. Uniform and hand-crafted grids can be suboptimal given a budget on the number of time steps. We introduce Adaptive Reparameterized Time (ART), which controls the clock speed of a reparameterized time variable to redistribute computation along the sampling trajectory while preserving the terminal time, with the objective of minimizing the aggregate Euler discretization error. We derive a randomized companion ART-RL that recasts ART as a continuous-time reinforcement learning problem with Gaussian policies, and prove a two-directional bridge between the two: the deterministic ART optimum lifts to an optimal Gaussian policy, and conversely any optimal Gaussian policy must recover the ART control through its mean. This bridge turns continuous-time actor--critic learning into a principled, rather than heuristic, route to the deterministic timestep optimum. Within the official EDM pipeline, ART-RL improves FID on CIFAR--10 across a wide range of budgets; after one-time offline training, the distilled deterministic schedule transfers without retraining to AFHQv2, FFHQ, and ImageNet at no extra inference cost.

cs.LG · cs.AI · eess.SY · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16483

Privacy-Preserving Range Counting over Local Graph Statistics

Gengda Zhao, Yizhang He, Dong Wen, Kai Wang, Ruijia Wu, Jianwei Wang

原文摘要

Range counting over vertex statistics supports contact analysis, community profiling, and reputation screening. However, releasing exact range counts can disclose sensitive edges to an adversary with background knowledge. We study this problem under edge local differential privacy (edge LDP), where each vertex randomizes its report before sending it to an untrusted curator. We develop a layered framework that connects statistic estimation, direct range estimation, and adaptive estimator selection. Statistic-estimation methods provide complementary scalar estimators with explicit loss analyses, while indicator-estimation methods privatize range indicators directly. We next propose Bounded Conditional Range Inversion (BCRI), which derives unbiased range estimates from reusable bounded geometric reports through an explicit local inverse. Its boundary-conditioned estimator achieves no larger expected L2 loss than the unbounded inverse, and histogram preprocessing supports constant-time queries. The same reports also provide a nonnegative, unbiased variance estimate for every answer. At the center of the framework, Adaptive Estimator Selection (AES) privately predicts bias, variance, and covariance to combine BCRI with a conditional estimator from the same transcript. Experiments on diverse real-world graph tasks demonstrate the accuracy, efficiency, and workload scalability of the proposed framework.

cs.DB

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.16372

Theorem-Proving Agent over Abstract Syntax Tree

Qiyuan Xu, Joshua Ong Jun Leang, Renxi Wang, Wenda Li, Haonan Li, Luke Ong, Conrad Watt

原文摘要

Interactive theorem proving (ITP) underpins program verification and formalized mathematics, but its manual effort limits scalability. LLM-based proof agents promise to ease this effort, but their heavy token consumption and API cost remain a major obstacle. We trace this cost to a shared root: current agents operate on serialized concrete syntax, emitting proofs as source text and recovering proof states through separate, line-number-based queries, so every edit shifts later lines and forces repeated relocation of errors and states. This same dependence on concrete syntax also blocks adoption of Minilang, a recent proof language that reaches SOTA on LLM-based proving but is too new for LLMs' training corpora. We address both problems by lifting the agent off source text and onto the abstract syntax tree (AST): the model supplies proofs as JSON representations of Minilang's AST -- native to tool-calling LLMs -- and drives the prover through a tree-edit model that fuses proof operations and states into one proof tree, so each operation carries its own subgoal's state, readable directly off the tree. We realize this design in \emph{Agent over AST} (AoA). Against Amazon's Isabelle Agent on miniF2F and NTP4VC-Pearl common success sets, AoA cuts API cost by 2.3--4.7x (normalized input-cache accounting), uses 2.9--6.9x fewer tokens and 3.9--8.9x fewer tool calls, and finishes 1.4--2.0x faster -- while also solving far more problems on the harder verification benchmark.

cs.SE · cs.AI · cs.LG · cs.PL

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新论文 · v1arXiv:2611.16478

One Model, Two Precisions: Does Disagreement Between Quantized Copies Tell You When a Small Model Is Wrong?

Mohammed Nayeem Khan, Allan Kevin T

原文摘要

Local language models often exist at several precisions, and quantization is known to change individual answers. This study asks whether the disagreement between two precisions of the same weights, an 8-bit copy that answers and a lower-precision copy that reads the same prompt, works as a per-question error score for abstention, escalation and re-asking. The cost is one extra forward pass over fewer bytes, with no sampling and no training. We simulate round-to-nearest quantization with per-group scales (3 to 6 bits, groups of 32 to 256) on Qwen3-0.6B and Qwen3-1.7B and score 8,398 multiple-choice questions from ARC-Easy, ARC-Challenge, MMLU and HellaSwag subsets. Error detection is measured by AUROC, risk-coverage curves and ECE on a held-out half, with paired bootstrap intervals over questions. For Qwen3-0.6B, the entropy of the 8-bit answer reaches AUROC 0.682; the best disagreement score, selected on a development half, reaches 0.684 (+0.001 [-0.009, +0.011]), and combining it with the confidence features adds +0.009 [+0.002, +0.015], mostly on ARC. On Qwen3-1.7B the selected score is -0.001 [-0.009, +0.006] from entropy and the combination +0.003 [-0.001, +0.006] on the test half. Gaussian weight noise at matched error energy is about as informative as rounding (0.6B only), so we find no evidence that the signal is specific to quantization, and shuffling the features within task and entropy cells removes the gain. One extra 8-bit pass that shifts the option order does better (+0.049 [+0.031, +0.067] over entropy) at about a third more weight traffic; routing to a larger model on free entropy beats both extra-pass scores at equal total traffic. After logistic recalibration the 0.6B confidences are calibrated within a few points of ECE. A deployment recipe and the cases where the method does not help are given.

cs.AI

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新论文 · v1arXiv:2611.16477

Where in the Input Does a One-Pass Decision Encoder Look?

Mohammed Nayeem Khan, Allan Kevin T

原文摘要

Laya is a small model that answers a multiple-choice question about a text in one pass: the question, the options (each with a name and a description) and the text form one sequence of at most 512 tokens, the window, in the English release. A longer text is silently cut at the end, and we measure what that costs. Test documents place a held-out row's text, the evidence, at a chosen token offset among distractor text; 177 items, five tasks, three checkpoints (released, short-row fine-tune, long-document fine-tune), four window sizes and 93,275 scored requests. When the evidence lies beyond the window, accuracy falls to 26.4% for the released checkpoint (27.2% for both fine-tunes), near the 27.0% of guessing and below the 35.6% of always giving the commonest label. Confidence gives no warning (AUROC 0.50 to 0.55 for telling cut from read requests; 0.5 = chance), and the runtime's truncation flag also fires on requests whose evidence was visible. The released checkpoint reads visible evidence badly once text precedes it: 38.5% pooled against 60% when the evidence comes first, so a larger window alone gains +6.2 points. Fine-tuning on long documents adds +27.6 points on visible evidence over the short-row fine-tune (95% interval +22.3 to +33.1) and nothing on cut evidence. That is an in-family result: training rows and test documents share generator and question types, and the main filler is of the training kind. With encyclopedic filler never seen in training the gain is +13.2 (+9.9 to +16.6), with rows of other tasks as filler -2.0. Among training-free rearrangements a 312-token window scan helps most (+11.9 points over the default for the released checkpoint) at six to eleven forward passes. Documents are synthetic, labels are LLM-made and one backbone is studied, so these diagnose this model, not long documents in general.

cs.CL · cs.AI

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版本更新 · v2arXiv:2607.11607

When Can Learned Uncertainty Be Trusted? An Audit of Distributional Reinforcement Learning

Hari Prasad

原文摘要

Many learned predictive models represent their uncertainty as a probability distribution, and planners and policies use this distribution to judge risk. However, the fact that a model outputs a distribution does not tell us whether that distribution captures the uncertainty in the environment which is relevant for a decision. In this work we study this question in distributional reinforcement learning, where the learned return distribution P_θ(Z | s,a) can be compared directly with an environment-grounded distribution P_env(Z | s,a) estimated by snapshot-restart Monte Carlo. Our audit first screens for the claims that a risk-sensitive user would act upon, using the excess Wasserstein gap between the top two actions (which is equal to the mass by which first-order stochastic dominance is violated). Each claim is then tested against ground truth using permutation nulls, bootstrap refutation and FDR control. Across QR-DQN, C51 and IQN on MinAtar (33 runs), 40-95% of the strongest claimed risk trade-offs are refuted at 95% confidence, their placement is statistically indistinguishable from a truth-blind placement, and almost none of them can be confirmed. A similar pattern is seen for a pretrained full-Atari QR-DQN. On positive controls of known magnitude the same audit confirms 96-100% of real claims (correlation 0.89-0.92), so it is able to detect real risk when it is present. Acting on the learned CVaR advice at the most-flagged states gives results ranging from beneficial to significantly worse than chance. Ensembling, risk-sensitive training and recalibration do not fix the problem, and the recalibration experiment indicates that the learned spread has no usable information about the true risk. Based on these results, we suggest that uncertainty estimates, including those of learned world models, should be checked against realized outcomes at the decisions where they are used..

cs.AI · cs.LG · stat.ML

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版本更新 · v2arXiv:2602.10811

EST: Towards Efficient Scaling Laws in Click-Through Rate Prediction via Unified Modeling

Mingyang Liu, Yong Bai, Zhangming Chan, Sishuo Chen, Xiang-Rong Sheng, Han Zhu, Jian Xu, Xinyang Chen

原文摘要

Efficiently scaling industrial Click-Through Rate (CTR) prediction has recently attracted significant research attention. Existing approaches typically employ early aggregation of user behaviors to maintain efficiency. However, such non-unified or partially unified modeling creates an information bottleneck by discarding fine-grained, token-level signals essential for unlocking scaling gains. In this work, we revisit the fundamental distinctions between CTR prediction and Large Language Models (LLMs), identifying two critical properties: the asymmetry in information density between behavioral and non-behavioral features, and the modality-specific priors of content-rich signals. Accordingly, we propose the Efficiently Scalable Transformer (EST), which achieves fully unified modeling by processing all raw inputs in a single sequence without lossy aggregation. EST integrates two modules: Lightweight Cross-Attention (LCA), which prunes redundant self-interactions to focus on high-impact cross-feature dependencies, and Content Sparse Attention (CSA), which utilizes content similarity to dynamically select high-signal behaviors. Extensive experiments show that EST exhibits a stable and efficient power-law scaling relationship, enabling predictable performance gains with model scale. Deployed on Taobao's display advertising platform, EST significantly outperforms production baselines, delivering a 3.27\% RPM (Revenue Per Mile) increase and a 1.22\% CTR lift, establishing a practical pathway for scalable industrial CTR prediction models.

cs.IR

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新论文 · v1arXiv:2611.16472

RGB-D SLAM on the Edge: Benchmarking and Profiling RTAB-Map and ORB-SLAM3 on the Raspberry Pi 5

Himanshu Patil, Jaideep Joshi, Rohit Saluja, Ganesh Ramakrishnan

原文摘要

Micro aerial vehicles that operate without GPS often run SLAM on ARM-based edge boards, yet there is little published data on how standard RGB-D SLAM systems behave on such hardware. We benchmark RTAB-Map and ORB-SLAM3 on the Raspberry Pi 5 using six TUM RGB-D sequences at three image resolutions, recording tracking coverage, trajectory error, time per frame, memory, CPU load and temperature for every run, and we profile both systems at the stage and function level. RTAB-Map was configured to process and keep every frame, so its back end runs on every frame rather than at its default rate of 1 Hz. On the sequences that every configuration tracked, its front end takes 38-74 ms per frame and its full pipeline 150-462 ms, compared with 13-45 ms and 26-62 ms for ORB-SLAM3. RTAB-Map's main cost is optimizing a pose graph that keeps growing, because its working memory is unbounded by default; ORB-SLAM3's local bundle adjustment costs about as much as its tracking but runs in a parallel thread. At quarter resolution, both systems lose tracking on most sequences, and runs that lose tracking finish sooner and report lower error on the poses they keep, so time and accuracy must be read alongside coverage. Timing is repeatable to within 1-5%, but trajectory error varies by up to 27% between identical runs, so the accuracy differences between the two systems are inconclusive. No thermal throttling occurred.

cs.RO

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新论文 · v1arXiv:2611.16471

Accuracy Is Not Welfare: Evaluating AI Buyers under Strategic Price Complexity in a Synthetic Duopoly

Ning Jiaqu

原文摘要

AI buying agents may alter sellers' incentives as well as consumers' decisions. In a synthetic duopoly, we evaluate price-comparison pipelines on a fixed menu and in empirical correlated-equilibrium games. Calculator assistance selects an optimal offer 84.00% of the time, versus 68.57% under verified-total disclosure, yet disclosure has a 20.14-unit higher consumer-worst equilibrium surplus. A frozen-strategy holdout comparison favors disclosure, while re-solving equilibria under scenario resampling produces a range spanning zero. These model-based results show why accuracy, loss severity, seller incentives, and welfare incidence require separate measurement.

cs.GT · econ.GN · q-fin.CP

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新论文 · v1arXiv:2611.16467

SS-HSDP: Efficient and Flexible Sharded Data Parallelism via Runtime-Update Decoupling

Chendong Xiang, Pengle Zhang, Jinliang Li, Zichen Liang, Jintao Zhang, Fan Bao, Jun Zhu, Jianfei Chen

原文摘要

Sharded data parallelism enables memory-efficient training of large models. While efficient training kernels favor packed, kernel-specific data layouts, optimizers may require variant data structures as optimization units. Existing sharded data-parallel frameworks couple these two choices, limiting runtime efficiency and optimization flexibility. To address this, we present \textbf{\our}, a training framework that separates runtime parameters from update parameters, allowing computation layouts and optimization units to be organized independently. Through shared data views and hierarchical sharding, \our supports this separation while avoiding redundant data movement. This design supports a broad range of optimizers, including matrix-based methods such as Muon, and performance optimizations such as fully zero-copy execution. Experiments on video and language model workloads with MXFP8 training show that \our achieves higher Model FLOPs Utilization (MFU) than FSDP2, veScale-FSDP, and Megatron-FSDP. At 128 GPUs, MFU improves by up to 21.7\% over the fastest evaluated baseline, with comparable or lower peak memory usage.

cs.DC

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版本更新 · v2arXiv:2609.38372

Multi-Task Self-Evolving Harnesses with the Agent as Its Own Optimizer

Qiankai Xu

原文摘要

A harness is the code around a language-model agent that organizes prompts, calls tools, manages context, and controls execution. As models grow stronger, recent work has begun to let agents improve their own harnesses, a line of work known as self-evolving harnesses. In most existing methods, a separate proposer running on a human-designed harness modifies the solver's harness, and a separate harness is evolved for each benchmark. Real-world tasks come from many domains, so both the evolution and the evaluation of a harness should cover a diverse range of tasks. We propose a framework close to recursive self-improvement: the same frozen model, on the same version of the harness, first solves tasks as the solver and then, as the proposer, reads the complete run records and directly edits the harness that runs it. Each evolution batch draws tasks from five benchmarks in different domains. To measure generalization, training and held-out tasks are strictly separated, and we additionally evaluate on five out-of-distribution benchmarks never used during evolution. We frame the evolution process as deep-learning training with two stages, multi-task pretraining and continual training. Starting from a 49-line seed harness, the harnesses evolved by GLM-5.3 and GPT-5.6 Sol gain about 5 points on the in-distribution benchmarks, matching or exceeding Claude Code and Codex, and the GPT-5.6 Sol harness is on par with Codex on five out-of-distribution benchmarks. Continued evolution on Claw-Eval in the second stage then lifts GPT-5.6 Sol and GLM-5.3-Flash above Codex and Claude Code. We also provide an in-depth analysis of the mechanisms that emerged during evolution, including output truncation, history compaction, completion checks, independent review, and visual inspection. Code and the evolved harnesses are available at https://github.com/xqkkk/self-evolving-harness.

cs.AI · cs.LG

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新论文 · v1arXiv:2611.16466

How Should Planning Agents ReSHAPE Their Search Space?

Yilin Zhang, Xinran Zhao, Tong Chen, Ziming Yuan, Tongshuang Wu

原文摘要

Planning agents and benchmarks treat a user's initial specification as fixed. Yet the requests users write exclude plans they would gladly take, since they rarely discover the possible trade-offs they would accept until seeing the options. Agents already revise requests through interaction, but whether this helps planning is unknown. We formalize this step as the ReSHAPE task, which judges a revision by whether its search space matches the acceptable plans and a planner finds one there. Using ReSHAPE Planter, which builds instances from planning benchmarks with a human-validated simulated user, we analyze 5,800 agent-revised specifications in travel and shopping. We find that reshaping matters: one correct trade-off makes the same planner reach an acceptable plan up to 4.6x as often, and search space quality predicts what the planner finds. Current agents, however, admit at most 32% of the acceptable plans, where that trade-off alone admits over 52%, because they ask about preferences instead of showing plans; in travel, showing plans raises F1 by 9.7 points over asking, with two model families. Planning agents should thus treat a request as a hypothesis to test against the user's reactions to concrete plans to recognize user trade-offs.

cs.AI · cs.CL

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