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版本更新 · v4arXiv:2606.14299

What Drives Test-Time Adaptation for CLIP? A Controlled Empirical Study from an Update Perspective

Jiazhen Huang, Xiao Chen, Zhiming Liu, Yaru Sun, Jingyan Jiang, Zhi Wang

原文摘要

Vision-Language Models (VLMs) such as CLIP have become a standard backbone for open-vocabulary recognition, yet their zero-shot predictions remain vulnerable to distribution shifts encountered at deployment. Test-Time Adaptation (TTA) has recently been extended to CLIP as a lightweight solution, leading to a rapidly growing body of TTA4CLIP methods. However, empirical progress in this area has largely outpaced our understanding of what truly drives adaptation, where their gains originate, and under which shifts they remain reliable. In this paper, we take a step back from the pursuit of state-of-the-art accuracy and conduct a systematic controlled study of TTA4CLIP. We first organize existing methods into three unified paradigms according to what is updated at test time. We then introduce TTABC, an open-source TTA Benchmark for CLIP, which standardizes evaluation protocols and integrates more than 20 representative methods. Our controlled empirical analysis focuses on three key areas. First, we determine the driving factors in parameter-based methods, revealing that adaptation gains are primarily driven by test-time evidence and reliable proxies rather than heavy optimization. Second, we explore evidence utilization beyond heavy parameter tuning, showing that competitive and efficient performance can be achieved through cross- or current-sample evidence and lightweight prototype updates. Finally, we demonstrate that there is no silver bullet for TTA: no single adaptation paradigm is universally optimal, and the preferred paradigm depends on the nature of shift. We hope our benchmark and study provide a clearer understanding of the current TTA4CLIP landscape and establish a foundation for further research. The project page is available at https://walawalagoose.github.io/TTABC/

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10678

MGE: A Membership Guided Data Extraction Attack against Diffusion Models

Yuchang Mei, Longqiang Wang, Haoyang Jiang, Mingyang Yi, Shaolei Zhang, Keman Huang, Wuqiong Pan, Ju Fan

原文摘要

Recently, diffusion models have achieved remarkable performance in image generation. However, their inherent memory properties introduce a potential safety risk of training data leakage, i.e., data extraction attack. To illustrate such risk, we propose a membership guided extraction (MGE) framework to effectively extract training data from a pre-trained diffusion model. Our core idea is to formulate the data extraction as a conditional generation, where the condition signal is from a membership inference classifier. To improve the success rate and efficiency of MGE, we propose adversarial gradient and parallel branch decoding. Our experiments on image generation benchmarks demonstrate that our MGE can extract hundreds of images, which shows that more attention should be paid to privacy-preserving of diffusion model.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10660

Dynamics-Informed Action Learning for Goal-Conditioned Latent Planning

Yinuo Jiang, Guangzhi Wang, Yiran Wang, Junfu Pu, Cheng Cheng, Honghao Chen, Cheng Cheng, Ying Shan

原文摘要

Latent world models enable visual control by planning action sequences in compact representation spaces, but a gap remains between how latent dynamics are learned and how they are used for planning. First, teacher-forced training does not expose the dynamics model to the recursively predicted latent states encountered during planning. Second, sampling-based planners typically initialize action search from goal-agnostic proposals, without exploiting the action-conditioned dynamics learned by the world model. We introduce Dynamics-Informed Action Learning (DIAL) to bridge these two gaps. During training, rollout alignment exposes the forward model to its own autoregressive latent predictions, while multi-step inverse supervision trains a lightweight inverse dynamics head to infer actions from latent-state transitions. We further apply the inverse head to model-generated transitions with its parameters held fixed, encouraging predicted rollouts to retain action-relevant information. At test time, DIAL autoregressively couples the inverse and forward dynamics to construct a dynamics-informed action sequence toward the goal, which initializes the mean of the Cross-Entropy Method (CEM) proposal distribution. The subsequent CEM search then refines this initialization using the standard sampling and updating procedure. Experiments across four visual control benchmarks demonstrate that DIAL outperforms existing latent world models and planning baselines in both standard and long-horizon scenarios.

cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.14499

Contextualized Evaluation of Vision Language Models through Dynamic, Multi-turn Interactions

Yijiang Li, Huiqi Zou, Bingyang Wang, Ziang Xiao

原文摘要

Multi-modal Large Language Models (MLLMs) have made substantial advances on benchmarks, yet their real-world effectiveness remains uncertain. This gap stems from the fundamental misalignment between benchmarks in controlled, static settings and the dynamic, interactive, and contextualized nature of real-world applications. To bridge this gap, we propose CEDI (Contextualized Evaluation of MLLMs through Dynamic, multi-round Interactions), a framework that recasts evaluation as a three-party interaction between an evaluatee model, an automated examiner, and a grader. The examiner conducts multi-turn, semi-structured conversations guided by a graph-based representation of the task. By navigating state-space transitions, CEDI deploys diverse strategies, from clarification requests to adversarial probes, to elicit performance evidence. We apply CEDI to visual hallucinations. Empirical results across multiple models, diverse settings, datasets, and domains show that contextualized, interactive evaluations reveal not only significantly more hallucinations than conventional static evaluation but also ones that more closely resemble those arising in practical use cases. We further show that hallucinations often accumulate over long contexts, through self-reinforcing dialogue history, and models are particularly vulnerable to questions requiring premise rejection or refusal. Together, these findings highlight CEDI as a step toward realistic, systematic, and ecologically valid assessments of MLLMs' capabilities. Code is available at github.com/williamium3000/cedi.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10617

NeuroHorizon: Long-Horizon Forecasting of Neural Population Activity with Hierarchical Memory

Peng Jiang, Xiaoxuan Jia

原文摘要

Long-horizon forecasting of neural population activity tests whether a model has captured dynamics that remain predictive well beyond the observed history. Prediction quality typically declines as the horizon grows, yet neither short-horizon accuracy nor teacher-forced evaluation, which supplies the true activity preceding each predicted step, reveals how well models sustain predictions from an initial history alone. We compare direct multi-step prediction and autoregressive rollout on spiking recordings across horizons and introduce NeuroHorizon, which pairs event-based spike encoding with a causal decoder for population firing rates. Its hierarchical prediction memory keeps recent predictions at full temporal resolution and compresses earlier ones into segment summaries, giving the decoder both local detail and longer-range context. Under teacher-forced training this memory holds ground-truth activity, whereas during rollout it holds the model's own predictions. To reduce this mismatch, we train with scheduled sampling, progressively exposing the decoder to its own predictions. From 500 ms of macaque motor-cortex activity, NeuroHorizon achieves the highest forward-prediction score among the evaluated autonomous models at all four horizons from 100 to 1000 ms, and its per-bin predictions remain above a mean-firing-rate baseline across nearly the full 1 s window. Complementary experiments on macaque sensorimotor and mouse multi-region recordings show that performance generally increases with model and data size, alongside improved prediction after cross-subject pretraining and fine-tuning. Together, these results clarify how forecasting performance changes across horizons and offer an effective approach to long-horizon forecasting of neural population activity.

q-bio.NC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.23180

TriSpec: Ternary Speculative Decoding via Lightweight Proxy Verification

Haoyun Jiang, Junqi He, Feng Hong, Xinlong Yang, Jianwei Zhang, Zhengyang Zhuge, Zheng Li, Xiaofeng Cao 等 12 位作者

原文摘要

Inference efficiency in Large Language Models (LLMs) is fundamentally limited by their serial, autoregressive generation, especially as reasoning becomes a key capability and response sequences grow longer. Speculative decoding (SD) offers a powerful solution, providing significant speed-ups through its lightweight drafting and parallel verification mechanism. While existing work has nearly saturated improvements in draft effectiveness and efficiency, this paper advances SD from a new yet critical perspective: the verification cost. We propose TriSpec, a novel ternary SD framework that, at its core, introduces a lightweight proxy to significantly reduce computational cost by approving easily verifiable draft sequences and engaging the full target model only when encountering uncertain tokens. TriSpec can be integrated with state-of-the-art SD methods like EAGLE-3 to further reduce verification costs, achieving greater acceleration. Extensive experiments on the Qwen3 and DeepSeek-R1-Distill-Qwen/LLaMA families show that TriSpec achieves up to 35% speedup over standard SD, with up to 50% fewer target model invocations while maintaining comparable accuracy.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10609

Reason First, Retrieve What Matters: Hypothesis-Guided Keyframe Selection for Long Video Understanding

Yisong Hu, Junbo Wang, Yuke Li, Jiangbin Zheng

原文摘要

Long video understanding requires models to identify and reason over sparse, temporally distributed evidence within limited frame budgets. Existing keyframe selection methods typically retrieve query-relevant frames from a densely sampled candidate set before inference. However, global image-text matching incurs unnecessary computation when a small set of uniformly sampled frames already provides sufficient evidence. Query relevance alone may also overlook evidence that distinguishes competing answers, while one-shot selection offers no opportunity to correct earlier selection errors. We propose HypoKFS, a training-free, plug-and-play framework that couples keyframe selection with iterative inference by multimodal large language models (MLLMs). HypoKFS first reasons over sparsely sampled frames to construct an initial hypothesis space. The resulting answer distribution determines whether additional frame selection is needed. When needed, the retained hypotheses guide the selection of discriminative frames, followed by renewed inference and hypothesis refinement. This iterative process directs additional visual computation toward unresolved hypotheses while avoiding unnecessary global frame matching. Experiments on Video-MME, LongVideoBench, and MLVU demonstrate consistent improvements across multiple MLLM backbones. HypoKFS improves accuracy by up to 6.7 percentage points and achieves 72.9% accuracy on Video-MME at only 1.3% of the reference frame-hypothesis matching cost.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.12195

IF:CARGO: LLM-Based Semantic Compilation for Al-Native Rule Programming Games

Ting-Chen Hsu, Lianye Zhang, Jiangxu Lin, Zhaoyi Yu, Fei Qin, Zihao Chen

原文摘要

This case study presents IF: CARGO, an experimental puzzle game that uses a large language model as a semantic compiler rather than an autonomous game-playing agent. Players author IF/THEN rules in natural language, which the model translates into a constrained command schema for deterministic validation and execution by the game engine. This architecture creates a playable loop of expression, execution, observation, and revision, framing AI interaction as semantic debugging. A mixed-methods playtest with 24 participants across eight levels examined player attempts, thinking time, perceived controllability, adjustability, and interpretations of the AI's role. Results suggest that players generally understood the model as a translation intermediary and could revise their strategies through feedback, while periodic commands, multi-robot coordination, and rule-priority mechanics created greater cognitive and diagnostic demands. The study proposes a practical pattern for AI-native gameplay: constrain natural-language input, preserve player authorship, and ensure deterministic execution.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.36637

Decompose Dynamics Before Learning Dependencies in Spatiotemporal Systems

Ziqi Wang, Daojiang Hu, Cheng Bao, Zhiwei Ling, Wenzhuo Qian, Jiahui Zhai, Hailiang Zhao

原文摘要

Relations in networked spatiotemporal systems are often learned from observations that entangle dynamics governed by different mechanisms, obscuring what evolves locally and how it propagates across nodes. We introduce Component-Aware Network Dynamics with Ordered Relations (CANDOR), which decomposes local dynamics before learning their dependencies. CANDOR represents each trajectory through a persistent background, gradual accumulation and release, and sparse shocks. Conditioned on these components, a delay-aware physical branch models edge and sample-dependent propagation over directed topology, while a topology-unconstrained functional branch discovers latent dependencies from background dynamics. Context-adaptive fusion combines functional, forward-propagation, and reverse-support forecasts, with training objectives encouraging specialized and semantically consistent representations. Experiments on two traffic benchmarks and three long-horizon water-quality datasets span two distinct spatiotemporal systems: human-driven urban traffic and naturally evolving river water quality. CANDOR achieves the lowest aggregate normalized error across five datasets, outperforming the overall runner-up by 7.25%. These results establish decomposition before dependency learning as an effective principle for spatiotemporal representation learning.

cs.CE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.17253

Co-RL: Unsupervised Reasoning Emerges from Diverse Cohort in Multi-agent RL

Yunhao Yang, Yuexin Bian, Yunjie Tian, Di Fu, Tianjin Huang, Yuanyuan Shi, Ziang Xiao, Nuno Vasconcelos 等 9 位作者

原文摘要

Reinforcement Learning (RL) has emerged as a powerful approach for improving reasoning in language and vision-language models, yet its strongest successes still depend heavily on ground-truth supervision (e.g., verifiable reward). Such annotations are costly to obtain and become increasingly scarce as reasoning capabilities advance beyond what humans can reliably evaluate. Self-rewarding RL reduces this dependence by enabling models to derive reward signals from their own completions. However, training solely on self-generated feedback can reinforce existing biases, reduce response diversity, and ultimately lead to homogenized responses and training collapse. In this work, we show that unsupervised reasoning can emerge through cooperative multi-agent training. We introduce Co-RL, a framework in which multiple decoupled models, sharing no parameters, are simultaneously optimized through RL using rewards derived from the other agent. We further show that increasing cohort diversity, through heterogeneous model families, sizes, and rephrased training samples, reduces the correlated errors that drive self-reinforcing feedback loops. This diversity consistently improves reasoning performance and mitigates training collapse. Without any ground-truth labels, Co-RL improves the base models by 3.0-7.9% on average across seven benchmarks and by 1.6-4.4% across four multimodal benchmarks. It also outperforms the prior state-of-the-art multi-agent RL method CoMAS by 4.0% and the self-rewarding vision-language RL method MM-UPT by 2.0%. Our code is available at https://github.com/Diverse-CoRL/Co-RL and website at https://williamium3000.github.io/Co-RL.

cs.LG · cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10521

TimeHF 2.0: Multimodal, Multivariate, and Multi-Region Collaborative Forecasting for Large-Scale Retail Supply Chains

Zhongxuan Han, Zhengxin Shi, Kunxiu Chen, Xiaolin Huang, Yulin Huang, Anni Zhang, Xiaoming Lin, Zhengyu Chen 等 33 位作者

原文摘要

Large-scale retail supply chains need sales forecasts for specified products, future dates or planning windows, and regions under the conditions available at the forecast origin. Regional sparsity, changing environments, volatile sales, and unfamiliar business situations make these dimensions difficult to model together. We introduce TimeHF 2.0, a multimodal, multivariate, and multi-region collaborative forecasting framework that makes flexibly expressed business knowledge usable in numerical prediction. A coverage-aware directed encoder integrates regional sales and applicable covariates. A semantic alignment module transfers language-model hidden representations of natural-language plans and domain experience into target-specific forecasting context. Learning against sales observations connects these meanings to numerical evidence, allowing business users to supply experience, future plans, and changing circumstances without defining a new numerical feature for each description. Text-conditioned future queries and shared/routed experts adapt prediction to the supplied context. An ordered-distribution head allocates fine resolution to routine fluctuations and coarse resolution to extreme sales movements, while daily/window learning connects forecasts to cumulative planning quantities. Online parallel forecasting at JD yields relative accuracy improvements of 18.25% and 17.94% for national daily and planning-window sales, and 19.44% and 18.99% for DC daily and planning-window sales, over the original production deep learning baseline. Component comparisons, loss-design results, and product-type analyses examine the roles of regional information, covariates, business knowledge, context-dependent processing, and robust planning-oriented learning.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10518

Beyond Row Alignment: Virtual-Camera-Aware Online Stereo Rectification

Yizhao Peng, Rui Gong, Zaiwang Gu, Xudong Jiang, Jun Cheng

原文摘要

Stereo matchers estimate depth by comparing the left and right images from a stereo camera pair, but they assume that the two cameras are accurately calibrated and rectified. In real deployments, small physical shifts after calibration, caused by camera motion, mechanical drift, vibration, or installation changes, can break this assumption and degrade depth estimation. Online stereo rectification is therefore needed to correct the image pair during operation and recover a stereo geometry where standard stereo matching can work reliably. Recent online rectification methods mainly optimize row alignment, making the left and right views of the same scene point fall on the same image row. We show that this target, while necessary, is insufficient for deployable stereo depth estimation. A rectifier may make a small set of matched points look well row-aligned, but still distort the full images in ways that hurt stereo matching: useful image regions may be cut off, large blank areas may appear, objects may be stretched or squeezed, or image scale may change unevenly across the view. We propose VirtualRect, an online stereo rectification method designed for reliable real-world deployment. Rather than treating rectification as simply making the left and right image rows line up, this method estimates a rectified virtual stereo camera and combines image-wide supervision of image warp behavior. This makes the rectified images and the camera parameters come from the same geometry, so the images used for matching and the model used for depth computation remain consistent. Experiments on multiple datasets show that, compared with the prior state-of-the-art online rectifier, VirtualRect reduces vertical flow error by 17% on Carla-Flowguided and 7% on Semi-Truck Highway, and lowers depth MAE by 30-41% on Carla-Flowguided across three downstream stereo matchers.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.11928

AgentRobustBench: A Sim-to-Real Benchmark and Domain-Randomized RL Recipe for Tool-Use Agents

Xiaolin Zhou, Aojie Yuan, Zheng Luo, Zipeng Ling, Xixiao Pan, Yicheng Gao, Haiyue Zhang, Jiate Li 等 15 位作者

原文摘要

Tool-use language agents are evaluated on benchmarks that assume clean inputs, unambiguous tool registries, and reliable APIs. Real deployments violate all these assumptions: user typos propagate into hallucinated tool names, a misconfigured request timeout can stall an agent indefinitely, and duplicate tool names across servers can freeze an SDK. We study these failures as a sim-to-real gap in the tool-use partially observable Markov decision process (POMDP), where deployment noise enters through the observation, action space, reward-relevant metadata, or transition dynamics. We introduce AgentRobustBench, a benchmark with 22 perturbation types organized by these four POMDP components, each grounded in a verified GitHub issue or documented tool-calling failure. Across 21 models from 1.5B to 32B parameters (including the closed-source o4-mini), the robustness profile is sharply uneven: observation perturbations reduce accuracy by less than 5%, while reward-relevant and transition perturbations reduce accuracy by roughly 40% and 30%, respectively; scale alone does not close these gaps. We then propose ToolRL-DR, a domain-randomization reinforcement learning (RL) recipe that trains a tool-use agent on perturbation-augmented trajectories spanning the three statically encodable POMDP components. On a 3B backbone, ToolRL-DR-Full retains roughly three-quarters of clean accuracy and reaches an aggregate perturbed accuracy comparable to open-source 14B function-calling baselines while substantially narrowing the gap to o4-mini. It closes approximately 27% of the Transition gap despite never seeing transition perturbations in training, suggesting that RL on adversarial static tool-use inputs induces a more persistent retry policy that transfers to unseen runtime failures. Code, dataset, checkpoints, and leaderboard are accessible through our GitHub repository.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10356

SepLRM: A Foundation Model Paradigm for Recommendation

Di Chen, Xijun Xiao, Qin Ren, Weijiang Lai, Zhijian Pan, Zhentao Pan, Pengfei Nie, Cheng Yuan 等 26 位作者

原文摘要

Large recommendation models (LRMs) improve recommendation performance by scaling behavior sequence length, model capacity, and training compute. However, the return on investment (ROI) of continued scaling deteriorates as marginal gains diminish and long-sequence encoding costs rise, especially when this computation is repeatedly executed by distinct models for diverse recommendation stages and business scenarios. To address this issue, we propose SepLRM, a recommendation foundation model (RFM) paradigm that identifies reusable sequence encoding and reorganizes multiple end-to-end (E2E) LRMs into one RFM and multiple independently optimized vertical models (VMs). The RFM adopts an encoder-decoder architecture and exports its final-layer encoder state as the shared key-value (KV) cache that is independent of downstream task-offered candidates. Unlike candidate-specific logits or aggregated user embeddings, the KV cache preserves fine-grained behavioral information. Separation at this native KV interface also allows each VM to construct candidate-conditioned queries while retaining the same query-KV interaction as its E2E counterpart, enabling high-fidelity capability transfer. To ensure both transfer effectiveness and system efficiency, we co-design cache compression, the training and serving workflows, and LG-Store, a distributed storage system for KV caches. Experiments on three large-scale datasets show that SepLRM achieves state-of-the-art offline performance with a transfer rate of approximately 100%. It also demonstrates strong transferability across model iterations, recommendation stages, and business scenarios. Deployment in two large-scale industrial scenarios substantially improves the ROI of sequence scaling and delivers consistent gains in online A/B tests at ByteDance.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.01507

MILD: Mediator Agentic System with Bidirectional Perception and Multi-Layered Alignment for Human-Vehicle Collaboration

Jiyao Wang, Yunbiao Wang, Yubo Jiao, Xiao Yang, Dengbo He, Sasan Jafarnejad, Luis Miranda-Moreno, Raphael Frank 等 9 位作者

原文摘要

Prior studies report that partial driving automation can increase the cognitive demands on human drivers. This effect largely arises from human drivers' lack of transparent insight into the vehicle's intentions and decision logic, as well as from automated systems' limited awareness of the driver's dynamic state and preferences. This bidirectional misalignment undermines shared situational awareness and exacerbates coordination failures in human-vehicle interaction. To address these limitations, we argue for a paradigm shift that elevates the human role from passive supervisor to active manager. We introduce the Mediator-in-the-Loop-Driving system, based on an agentic system architecture to facilitate synergistic human-vehicle collaboration. MILD integrates a perception agent for joint in-cabin and out-of-cabin understanding with a lightweight strategy agent that generates compliant and explainable action suggestions. To ensure these strategies are strictly aligned with safety regulations and human values, we develop Evidence- and Constraint-weighted Policy Optimization. ECPO leverages automatic validators to steer the agent toward behaviors that are not only accurate but also structurally complete, substantiated by evidence, and less prone to constraint violations. Furthermore, a retrieval-augmented generation module dynamically incorporates constraints from traffic regulations, speed recommendations, and driver preferences into the decision loop. Experiments across three open datasets demonstrate that MILD consistently outperforms baselines in both perception accuracy and strategy quality under auditable offline metrics, and yields higher human-rated policy adequacy, comfort, and explanation than baselines.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10162

M5-TTS: A Mini-Scale Multilingual and Multi-Dialect Text-to-Speech Language Model with Multi-Token Prediction

Wenhao Li, Jingbin Hu, Shengming Ling, Xiaming Ren, Haoyu Zhang, Jinrui Liang, Hanke Xie, Qirui Zhan 等 23 位作者

原文摘要

We present M5-TTS, a compact text-to-speech system supporting multilingual and Chinese multidialect synthesis, zero-shot voice cloning, speaker-specific multilingual generation, and CPU streaming. Its 50M-parameter language model, excluding the speech codec, combines a temporal backbone with a local multi-token prediction module over 16 codebooks. GRPO post-training combines global speaker-similarity and pause feedback with a token-level CER process advantage. Character-level error comparisons provide local feedback, with negative credit propagated backward within a clause and projected onto audio-token positions. M5-TTS achieves 1.37% CER on Seed-ZH and 2.91% WER on Seed-EN, corresponding to relative error reductions of 62.2% and 35.3% over MOSS-TTS-Nano with half the language-model parameters. It also achieves lower content error rates in five of six evaluated MiniMax languages, while difficult Chinese inputs remain a limitation. On four Intel Xeon Gold 6530 cores, it achieves a real-time factor of 0.653 and a warm first-chunk latency of 225ms. A 10-listener evaluation gives M5-TTS higher speaker-similarity and intelligibility ratings than MOSS-TTS-Nano, with a lower naturalness rating. We will release training, post-training, and inference code, together with voice-cloning and speaker-specific checkpoints, to support reproducible research on compact speech generation for on-device voice interaction.

eess.AS · cs.SD

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.02945

面向AI辅助数学发现的持续图记忆与社区基础设施

Continual Graph Memory and Community Infrastructure for AI-Assisted Mathematical Discovery

Eric Hanchen Jiang, Junyi Zhang, Jinxi Yu, Jiachen Lu, Zhi Zhang, Michael Simon, Justin Baker, Richard Cai 等 30 位作者

AI 速读 基于摘要

作者提出Ansatz研究框架与Ansatze社区平台，用持续图记忆区分模型接受的结论与人工整理探索，并支持数学研究的检查、引导与形式化验证。

cs.AI · cs.CL

arXiv ↗PDF ↗
查看原文摘要

Mathematical discovery requires preserving partial ideas and the evidence supporting their reuse. We present Ansatz, a research harness whose continual graph memory separates model-accepted claims and dependencies from curated exploration, and Ansatze, a community platform for inspecting, guiding and checking this work. On First Proof, Ansatz recorded nine local verifier closures in each of two ten-problem batches; a partial result in the Second Batch also counted as a pass under the study's criterion. These model-assessed outcomes include continued searches and differ from expert validation. Component studies and re-review expose disagreements and their dependency reach. Open-problem investigations produced candidate results, with five Lean formalizations reported. The platform connects problem discovery, research records, manuscript analysis and formal checking while preserving distinct evidence states. Together, these systems support cumulative human-AI research through persistent investigation and inspectable contributions.

版本更新 · v2arXiv:2603.24500

Project and Generate: Divergence-Free Neural Operators for Incompressible Flows

Xigui Li, Hongwei Zhang, Ruoxi Jiang, Deshu Chen, Chensen Lin, Limei Han, Yuan Qi, Xin Guo 等 9 位作者

原文摘要

Learning-based models for fluid dynamics often operate in unconstrained function spaces, leading to physically inadmissible, unstable simulations. Penalty-based methods can reduce constraint violations but do not guarantee exact incompressibility. In this work, we introduce a unified framework that enforces the incompressible continuity equation as a hard, intrinsic constraint for both deterministic and generative modeling. We integrate a differentiable spectral Leray projection into neural operators to enforce incompressibility. For flow matching, we construct a divergence-free Gaussian reference measure via a curl-based pushforward, so that the reference samples, interpolation paths, and velocity targets are compatible with the projected dynamics. Experiments on 2D Navier-Stokes equations demonstrate exact incompressibility up to discretization error and substantially improved stability and physical consistency.

cs.LG · physics.flu-dyn

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2607.24532

From Machine Learning to Large-Scale EO Products: Good Practices and Guidelines for Making Maps

Ghjulia Sialelli, Robin Young, Cesar Aybar, Yuchang Jiang, Linus Scheibenreif, Damien Robert, Clemens Mosig, Kristof Van Tricht 等 15 位作者

原文摘要

The past decade has seen an explosion of large-scale geospatial products derived from Earth observation (EO) data using machine learning (ML). Driven by expanding model capacity and compute availability, the domain has reached a level of operational maturity where practitioners can generate high-resolution global products spanning thematic areas as diverse as human settlement, vegetation structure, and natural hazards. Deep learning has expanded the paradigm beyond locally calibrated models toward singular, globally applicable models trained on heterogeneous data, attracting a broad and growing community of practitioners from both the EO and ML sides. Yet, the resulting workflow introduces challenges that fall between established EO practice and mainstream ML: every stage presents tightly coupled technical challenges that recur across applications, and much of the practical knowledge needed to navigate them remains siloed within individual application domains. This guide brings together map-makers across the EO and ML communities, distilling their common practices, recurring pitfalls, and recommended approaches into a single end-to-end account, from raw satellite data to a published map product. Crucially, it treats the pipeline as an integrated workflow rather than a sequence of independent steps. We focus on wall-to-wall raster maps at moderate resolution (10-30m) and continental to global extent, produced with ML from open satellite archives, mainly Sentinel-1, Sentinel-2 and Landsat. The article is written for a deliberately broad audience, from those just entering the EO domain to ML experts building ML-ready EO datasets or generating large-scale maps.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.32813

HarnessMM: Recursive Self-Improvement for Harness Token Efficiency

Yuhua Jiang, Chang Xu, Feifei Gao, Li Zhao

原文摘要

HarnessMinusMinus (HarnessMM) is a research project on improving the efficiency of agent harnesses, the software coordinating model reasoning, tool execution, and feedback. This technical report consolidates its shared framework, implementations, and empirical studies across coding, tool-based, and visual-agent settings. Agent harnesses often invoke reasoning after each tool result, even when earlier calls have already determined subsequent actions. HarnessMM addresses this overhead through guarded decision reuse and recursive self-improvement (RSI). RSI refines author prompts, context settings, and adapter rules using training feedback, with validation selecting deployment configurations. At inference time, a controller reuses resolved decisions while keeping actor weights fixed. Executable Decision Graphs encode actions, arguments, dependencies, and guards. Guarded Decision Reuse activates eligible decisions from observed feedback, while Certified Callback Substitution executes them through native tool interfaces, replacing redundant reasoning callbacks. The original actor resumes when new reasoning is required or reuse conditions are unmet. On HumanEval, HarnessMM preserves Codex success at 90.82\% while reducing tokens per task from 66.72k to 47.02k (29.54\% saving). For DeepSeek Harness, success improves from 71.43\% to 85.71\% while tokens decrease from 96.28k to 11.62k (87.93\% saving). An MBPP ablation shows 26.91\% lower cost than single-use graphs and 19.52\% lower cost than retained callbacks. The report also summarizes the HarnessMM-V visual-agent study and documents optimization overhead and execution time where measured. Together, these studies investigate guarded decision reuse across diverse agent harness settings. Code is available at https://github.com/YuhuaJiang2002/HarnessMM.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.38238

Variational Consequence-Driven Offline Reinforcement Learning

Wen Jiang, Ke Jiang, Jin Wang, Xiaoyang Tan

原文摘要

Offline reinforcement learning (RL) typically mitigates distribution shift by imposing divergence constraints on the action distributions of the learned and behavior policies. However, this paradigm cannot ensure the underlying dynamic consistency structure: even minor action deviations can lead to drastically different transitions, inadvertently driving the agent away from the offline dataset. To address this, we explore policy constraints through their induced state-consequence distributions rather than relying on pure action-distribution matching. Nevertheless, directly enforcing such consequence-aware constraints is computationally intractable without access to the true underlying dynamics model. To bypass this issue, we propose Structural Behavior Regularization (StBR), which replaces the intractable objective with a closed-form latent divergence. Structuring this latent space to reflect transition dynamics renders the latent divergence a theoretically bounded surrogate for the true consequence-distribution divergence. By regularizing the learned policy with this latent divergence, StBR adaptively tightens the behavior constraint in dynamics-sensitive regions, effectively preventing deviations outside the offline dataset. Empirically, we demonstrate that StBR achieves superior performance across diverse offline RL benchmarks.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09548

CoMoFlow: Controllable Protein Conformational Ensemble Generation via Explicit Collective-Mode Modeling

Yusong Wang, Zheyuan Gu, Ailiang Lin, Minghao Shao, Yicheng Xu, Jiang Cai, Prayag Tiwari, Mingkun Xu

原文摘要

Protein conformational ensemble generation aims to efficiently characterize the structural variations accessible to a protein beyond a single static structure. Recent flow-based methods efficiently generate conformational ensembles, but they model coordinated structural variation implicitly, which can limit ensemble fidelity and hinder direct control. To address this limitation, we propose CoMoFlow, a controllable framework for generating protein backbone conformational ensembles by explicitly modeling collective variation. CoMoFlow constructs protein-specific collective modes from the equilibrium structure, learns the distribution of their coefficients across the molecular dynamics (MD) ensemble, and uses sampled collective variations to guide SE(3) flow matching through feature conditioning and prior recentering. Experiments on ATLAS and de novo proteins show that CoMoFlow improves reconstruction of MD conformational distributions and coordinated structural variation relative to strong flow-based baselines, while enabling direct control over targeted conformational changes.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.13799

Adaptive Variance Reduction across Smoothness and Noise Conditions

Wei Jiang, Yibo Wang, Sifan Yang, Lijun Zhang, Zechao Li

原文摘要

In this paper, we study adaptive stochastic non-convex optimization under unknown smoothness and noise conditions. Our algorithms require no smoothness constant, noise bound, initial function gap, or oracle-regime label. We first propose a fixed-budget method that combines normalized momentum, recursive variance reduction, and a fresh gradient stage, followed by fresh validation of the candidates. We prove that the same output has expected gradient norm $O(T^{-1/4})$ under objective smoothness and bounded noise variance, $O(T^{-1/3})$ under average smoothness, and $O(T^{-1/2})$ for noiseless gradients, where $T$ counts individual gradient evaluations. We then develop an anytime algorithm whose three optimization paths preserve their endpoints across reporting and budget-extension boundaries. The same three rates hold at every deterministic query budget, although the algorithm receives no information about the budget $T$. The analysis combines a scale potential, a uniform second-moment bound, and accounting for candidates retained through unfinished blocks. An optional sample-mean selector further reduces cumulative validation work from $O(T)$ to $O(T^{2/3})$ while preserving these rates.

math.OC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.26239

CoRS: Fast Neural Flexible Job-Shop Scheduling via Composite Interval State Reuse

Sutong Jiang, Peng Zhao, Xuan Wu, Yan Wang, Jiaqi Wang, Rui Zhang, Yuan Jiang, You Zhou 等 9 位作者

原文摘要

Neural solvers for the Flexible Job-Shop Scheduling Problem (FJSP) typically refresh graph representations and update feasibility after every operation--machine decision. However, only feasibility must remain exact at every step, whereas graph encoding is substantially more expensive and can potentially be refreshed less frequently. We present CoRS, which decouples them: an interval-level graph representation is reused across multiple sequential decisions, while a stepwise feasibility state is updated after every action. This two-timescale design amortizes graph encoding without parallelizing action generation or relaxing scheduling constraints. CoRS further develops a feasibility-preserving composite-interval decoding scheme with dynamic constraint tracking and boundary state synchronization, which reduces representation refresh frequency while maintaining continuous feasible schedule construction. Furthermore, to alleviate representation staleness caused by interval-level state reuse, CoRS introduces enhanced static features as a compensation mechanism. By providing more comprehensive structural information of the scheduling problem, these features improve the model's perception of overall problem characteristics. Extensive experiments on synthetic and multiple public FJSP benchmarks demonstrate that CoRS significantly reduces inference time while maintaining competitive solution quality, achieving favorable speed-quality trade-offs under tight wall-clock budgets and low-latency scenarios.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09380

SPSC: Zero-Shot Cross-Subject EEG-to-Image Reconstruction via Semantic Preservation and Subspace Calibration

Yutang Lu, Shuai Jiang, Fei Su, Zhicheng Zhao

原文摘要

Pretrained vision-language models and diffusion generators have driven rapid progress in brain-to-image generation, while EEG-based reconstruction is still constrained by low signal-to-noise ratio and cross-subject semantic shifts. Zero-shot cross-subject reconstruction poses even greater challenges: EEG semantic embeddings learned from source subjects deviate from the manifold of image embeddings of the unseen target subject, particularly when neither target labels nor target image embeddings are available. To address this issue, we introduce the Semantic Preservation and Subspace Calibration (SPSC) framework, which first uses Object Mask Contrastive Regularization (OMCR) with object identity for backend training, and then constructs Subspace Candidate Gate (SCG) to achieve label-free semantic correction for target subjects during inference. Specifically, OMCR regularizes the conditioning space with object-level positive samples to preserve object-level feature neighborhoods within the reconstruction prior and low-level branch, while SCG exploits training image prototypes and batch statistics of unlabeled target EEG to select candidates for each task within subspaces of image prototypes. Under the leave-one-subject-out (LOSO) setting across ten subjects on the THINGS-EEG dataset, SPSC improves the cross-subject reconstruction from 0.552 to 0.713 on CLIP and from 0.612 to 0.745 on Alex.5. These significant gains indicate that the semantically calibrated conditions can better support generation for the target subjects under label-free inference.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02975

Introducing Cluster Efficiency to LLM Serving: Pooling Economical Accelerators over Fast Interconnects

Youhe Jiang, Pavle Glušac, Binhang Yuan, Eiko Yoneki

原文摘要

Long-context large language model (LLM) serving places substantial demands on compute, memory capacity, and memory bandwidth. Economical accelerators can offer these resources at lower cost, but their limited per-device capacity requires pooling across devices, making communication overhead critical. We study cluster efficiency, measured in tokens per dollar and per joule, and show how low-latency, high-bandwidth interconnects enable economical accelerators to retain their cost advantage. Guided by a characterization of coding-agent workloads and accelerator scaling, we realize this design on the Tenstorrent Galaxy, a cluster of Blackhole chips. The serving stack combines integrated communication engines, pipelined ring transfers, communication-computation overlap, and device-side decode replay to reduce collective overhead. Across three dense models in BF16 and 8-bit configurations with 8K-256K-token contexts, the Galaxy achieves 1.07-2.24$\times$ the tokens per dollar of an eight-GPU RTX PRO 6000 Blackwell server under a three-year acquisition-and-electricity cost model. Emulating TCP-class communication on the same chips reduces throughput by 23.5-67.4% and eliminates the cost advantage in every evaluated configuration. These results demonstrate how fast interconnects translate the per-dollar resource advantages of economical accelerators into cost-efficient long-context LLM serving.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09335

Tight Variable-Occurrence Bounds and Exponential Separations for Moser--Tardos and WalkSAT on 3-CNF

Minbo Gao, Kun He, Tao Jiang

原文摘要

We establish a sharp variable-occurrence threshold for polynomial expected-time convergence for the Moser--Tardos algorithm and the pure random-walk WalkSAT on 3-CNF formulas. For every formula with $m$ clauses and at most two occurrences per variable, both algorithms terminate in $O(m)$ expected updates. This occurrence bound is sharp: we construct an infinite family with at most three occurrences per variable on which both algorithms require $2^{Θ(\sqrt{m})}$ expected updates. Surprisingly, this dynamical transition occurs strictly below the classical NP-completeness threshold at variable-occurrence degree four. We further establish exponential separations between the two algorithms on satisfiable 3-CNF formulas. On one family, WalkSAT terminates in $O(m^4)$ expected flips, whereas MT requires $2^{Ω(m)}$ expected resampling steps. On another, MT terminates in $O(m)$ expected resampling steps, whereas WalkSAT requires $2^{Ω(m)}$ expected flips. These constructions exploit the different ways in which single-variable flips and simultaneous resampling propagate local violations. Our results show that the algorithms' expected running times are incomparable even up to polynomial overhead on the same inputs.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10073

Noise-Regularized Training for Learned Image Compression

Renjie Zou, Zhiwei Huang, Dong Jiang

原文摘要

Learned image compression (LIC) is trained almost exclusively on clean images: the only stochastic perturbation in the loop is an additive uniform proxy injected in the latent domain to keep the entropy model differentiable. Outside compression, by contrast, a long line of work---from Tikhonov regularization and contractive autoencoders through denoising score matching and modern diffusion models---has established that Gaussian noise at the input of a network with a clean target acts as a structured probe of the data manifold rather than as data augmentation. Building on this view, we introduce noise-regularized training for LIC: at every training step we add Gaussian noise to the encoder input while keeping the distortion target, the latent uniform proxy, the hyperprior, and the entropy coding pipeline unchanged. The recipe is architecture-agnostic and adds essentially no training or inference overhead. Across backbones and operating regimes it yields consistent BD-Rate gains: -2.60% on a reproduced ELIC and -0.85% / -1.05% / -2.38% on the Base/Medium/Large scales of a variable-rate DMCI codec, with DMCI-Large reaching -22.02% against VTM-22.0. A second-order expansion of the noisy RD loss accounts for these gains: because the noise enters upstream of the analysis transform, it converts the clean RD objective into a structured $O(σ^{2})$ regularizer comprising an information-weighted encoder-Jacobian penalty (rate side) and a Frobenius contractive penalty on the end-to-end reconstruction map (distortion side). A finite-difference Lipschitz analysis shows corresponding sensitivity reductions of ~12% for the encoder and ~8% for the end-to-end codec, while an exact-JVP test further shows preferential contraction of high-information directions. Code and trained models are available at https://github.com/Googolxx/NoiseRegu-LIC.

eess.IV · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.18413

Adamas: Hadamard Sparse Attention for Efficient Long-Context Inference

Siyuan Yan, Guo-Qing Jiang, Yuchen Zhang, Xiaoxing Ma, Ran Zhu, Chun Cao, Jingwei Xu

原文摘要

Large language models (LLMs) now support context windows of hundreds of thousands to millions of tokens, enabling applications such as long-document summarization, large-scale code synthesis, multi-document question answering and persistent multi-turn dialogue. However, such extended contexts exacerbate the quadratic cost of self-attention, leading to severe latency in autoregressive decoding. Existing sparse attention methods reduce these costs by attending to only a subset of key-value (KV) tokens, but fixed or coarse-grained selection strategies can miss important tokens or retain substantial redundancy under constrained token budgets. We introduce \textbf{Adamas}, an accurate and efficient token-level sparse attention mechanism for long-context inference. Adamas applies the Hadamard transform, bucketization and 2-bit compression to produce compact representations, and uses lightweight distance-based estimation for efficient top-$k$ candidate selection. The selected positions are then used to gather the corresponding original KV states for sparse attention. Comprehensive evaluations show that Adamas maintains near-lossless performance within the effective context length of Llama-3.1-8B-Instruct. In efficiency evaluations, Adamas achieves up to $6.5\times$ self-attention speedup and $1.7\times$ end-to-end decoding speedup at a 32K context length. Adamas also maintains performance comparable to full attention on challenging long-form reasoning benchmarks, including AIME 2024 and AIME 2025.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.01869

WorldCoder-Bench: Benchmarking Physically Grounded 3D World Synthesis

Shuo Lu, Yinuo Xu, Kecheng Yu, Siru Jiang, Yongcan Yu, Yubin Wang, Haitao Yang, Yuxiang Zhang 等 11 位作者

原文摘要

Large language models (LLMs) are increasingly asked not only to write static interfaces, but to construct executable interactive worlds from natural language. Browser-native 3D, commonly built with Three.js, is a natural next frontier: generated programs must integrate assets, obey spatial and physical constraints, and keep user-facing controls synchronized with hidden runtime state. Existing web-generation benchmarks and evaluators, however, largely observe only pixels or DOM nodes, while the mechanics of a Three.js world unfold inside an opaque <canvas>. We introduce WorldCoder-Bench, a benchmark for autonomous, physically grounded 3D world synthesis. WorldCoder-Bench contains 2,026 expert-curated tasks across Simulation, Rendering, and Application scenarios, with optional .glb assets and hidden behavioral contracts. We further propose StateProbe, an execution-based protocol that probes generated programs in a controlled browser and verifies hidden, mutation-hardened contracts over runtime states and transitions. Beyond verification coverage, we report Return on Automation and Time Efficiency Multiplier to estimate quality-adjusted cost and time utility. The best verification coverage is 27.8% across nine models on WorldCoder-Benchcore and 19.9% across five on WorldCoder-Benchrob, with failures dominated by state-schema drift and broken interaction chains rather than missing scene elements. Utility metrics further show that cheap or fast models can still provide substantial value on easier domains. WorldCoder-Bench is available at https://github.com/shuolucs/WorldCoder-Bench.

cs.AI

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arXiv ↗PDF ↗