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版本更新 · v2arXiv:2604.17861

GPUOS: A GPU Operating System Primitive for Transparent Operation Fusion

Yiwei Yang, Xiangyu Gao, Yuan Zhou, Yuhang Gan, Yusheng Zheng, Andi Quinn

原文摘要

Modern deep learning workloads often consist of many small tensor operations, especially in inference, attention, and micro-batched training. In these settings, kernel launch overhead can become a major bottleneck, sometimes exceeding the actual computation time. We present GPUOS, a GPU runtime JIT system that reduces launch overhead using a persistent kernel architecture with runtime operator injection. GPUOS runs a single long-lived GPU kernel that continuously processes tasks from a host-managed work queue, eliminating repeated kernel launches. To support diverse operations, GPUOS uses NVIDIA NVRTC to just-in-time compile operators at runtime and inject them into the running kernel through device function pointer tables. This design enables operator updates without restarting the kernel or recompiling the system. GPUOS introduces four key ideas: (1) a persistent worker kernel with atomic task queues, (2) a runtime operator injection mechanism based on NVRTC and relocatable device code, (3) a dual-slot aliasing scheme for safe concurrent operator updates, and (4) transparent PyTorch integration through TorchDispatch that batches micro-operations into unified submissions. The system supports arbitrary tensor shapes, strides, data types, and broadcasting through a generic tensor abstraction. Experiments show that GPUOS achieves up to 15.3x speedup over standard PyTorch on workloads dominated by small operations, including micro-batched inference and attention patterns. GPUOS improves utilization while remaining compatible with the PyTorch ecosystem.

cs.DC · cs.OS

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2507.11662

Let's Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification

Moises Andrade, Joonhyuk Cha, Brandon Ho, Vriksha Srihari, Karmesh Yadav, Zsolt Kira

原文摘要

Verifiers--functions assigning rewards to agent behavior--have been key to AI progress in math, code, and games. However, extending gains to domains without clear-cut success criteria (e.g., computer use) remains a challenge: while humans can recognize desired outcomes, translating this intuition into scalable rules is nontrivial. Multimodal LLMs (MLLMs) offer a promising solution, given their world knowledge, human-preference alignment, and reasoning skills. We evaluate MLLM verifiers across web navigation, computer use, and robotics, spanning 13 models, 28 judge designs, diverse agent trajectories, and distinct downstream applications. We identify a critical limitation: a strong tendency for MLLMs to over-validate agent behavior, a phenomenon we term agreement bias. This bias is pervasive across models, is resilient to test-time scaling, and harms pipelines relying on MLLM judgments (e.g., self-improvement). We discuss principles for design and evaluation of verifiers, and introduce SGV, a method that improves verification by harnessing MLLMs' own capabilities via modulation of (un)conditional generation. First, an MLLM generates broad priors about desired behavior, independent of the data under evaluation. Then, conditioned on generated priors, it reasons over and judges a candidate trajectory. Our methods yield more human-aligned verifiers, with gains of up to 25pp in failure detection, 14pp in accuracy, and benefits extending to downstream applications. In self-improvement and online supervision, our verifiers boost task completion of a GUI specialist in OSWorld, a diffusion policy in robomimic, and a ReAct agent in VisualWebArena--surpassing the previous SOTA by 20pp. Finally, we release an updated version of VisualWebArena, featuring improved evaluators, high-fidelity environment parallelism with >10x speedups, and VWA-Lite, a 1/3-scale subset with comparable evaluation fidelity.

cs.AI · cs.CL · cs.LG · cs.MA · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.10375

CHARM: Certified Homology-Aware Reverse-scale Mapping via Diffusion-Guided Quotient Towers

Zhe Li

原文摘要

Conventional dimensionality-reduction methods optimize geometric fidelity but do not return auditable homology certificates or identify the input observations associated with nontrivial classes. Direct high-dimensional persistent-homology computation is costly, and its representatives are rarely linked to a shared planar view. We introduce Certified Homology-Aware Reverse-scale Mapping (CHARM), which augments two-dimensional coordinates with algebraically certified, sample-traceable structural records. Diffusion-guided cover proposals and homotopy-safe contractions compress the input neighborhood flag complex. A Cover Quotient Tower supports cross-scale representative tracking and exact $H_k$ certification when algebraic tests complete. Reverse-scale layout connects quotient-chain preimages to the same coordinates used for geometric exploration. Contraction preserves the input neighborhood flag-complex homotopy type; the quotient tower inherits a multiplicative 3-interleaving with coarse-hop Rips, and conditional distortion bounds connect it to original Euclidean Rips. In a fixed-seed benchmark of 22 datasets (41,637 samples), CHARM achieves the highest share of first- or second-place scores across 15 metrics. On nonsynthetic data, it ranks first or second on every metric and first on all four $H_0/H_1$ persistence-diagram metrics. CHARM couples strong nonsynthetic geometric performance with an auditable structural layer.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.14671

SPARE: Shared Perception and Adaptive Retrieval of Evidence for Streaming Video Understanding

Senbin Xu, Zijun Zhang, Xinhua Wu, Li He, Jubo Feng, Li Qiao, Zhen Gao, Yan Zhang 等 10 位作者

原文摘要

Streaming video understanding requires answering questions from the observed video, with evidence drawn from recent observations or earlier events. Historical context can improve recall but may interfere with judgments based on recent frames. Its usefulness depends on both when it is introduced and which clips are selected, while relevance alone does not guarantee sufficient evidence for an answer. We propose SPARE, a framework that combines adaptive historical retrieval with visual encoder sharing. A lightweight gate predicts whether recent observations provide sufficient evidence to answer the question. When additional evidence is predicted to be necessary, a multimodal embedding model retrieves relevant historical clips to supplement the current context. LoRA instruction tuning trains the understanding model to use recent observations and retrieved history jointly and abstain when the evidence remains insufficient. To reduce redundant visual parameters, the understanding and retrieval branches share a visual encoder while retaining separate language backbones. Four linear adapters align the shared features with the retrieval model through distillation at the visual token and final retrieval embedding levels. With visual-only inputs, SPARE achieves 77.78% on the evaluated Real-time and Backward task groups of OVO-Bench and 73.31% on StreamingBench. Compared with Qwen3-VL-8B-Instruct evaluated at 2 fps, SPARE improves accuracy by 20.68 percentage points on OVO-Bench and 14.15 percentage points on StreamingBench.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.13233

Fabrication of Complex Soft Robotics: Challenges, Solutions, and Optimization

Mohammed Abboodi

原文摘要

Manufacturing complex soft pneumatic actuators remains challenging because geometric fidelity, compliance, structural integrity, and airtightness must be achieved simultaneously. This study presents a manufacturing-focused evaluation of several fabrication routes for complex pneumatic structures, including heat-shrink forming, silicone casting, powder- and liquid-based additive manufacturing, and fused deposition modeling (FDM). The processes were assessed through process screening, baseline fabrication, failure analysis, and process improvement to distinguish inherent process limitations from correctable manufacturing defects. Heat-shrink forming was limited by geometric conformity, casting by mold accessibility and bonded interfaces, powder-based methods by residual material trapped within enclosed passages, and digital light processing by the material properties and post-processing requirements of the investigated system. FDM provided the most adaptable route because its dominant defects could be progressively reduced through process optimization. The results further showed that airtightness depends not only on nominal wall thickness but also on extrusion-path architecture, while support-free geometry is important when access for internal post-processing is limited. These findings establish a practical design-for-manufacturing approach in which process selection is guided by the compatibility between actuator architecture and manufacturing constraints. The proposed approach provides practical guidance for developing complex, flexible, and airtight soft pneumatic actuators for soft robotic applications

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.09505

Bias-Aware Adjoint Calibration and Optimal Control of Stochastic Multiscale Bioprocess Digital Twins

Keilung Choy, Wei Xie

原文摘要

We develop a bias-aware digital-twin calibration and control framework for multiscale bioprocess models within a biological systems-of-systems (Bio-SoS) paradigm. The digital twin is represented by a stochastic differential equation (SDE) model and calibrated from sparse observations using quasi-likelihood estimation and adjoint sensitivity analysis. SDE generator-based moment expansions characterize truncation-induced parameter bias, while forward-backward adjoints quantify how calibration uncertainty propagates to value functions and policy performance. The resulting parameter-error distribution supports both policy-directed data acquisition and uncertainty-aware policy optimization through a second-order Gaussian-averaged objective. We characterize the asymptotic behavior of the resulting exploration criterion and derive a physical-system performance under the optimized policy. To implement these ideas, we develop an Actor-Simulator algorithm that jointly updates model parameters, selects informative experiments, and optimizes control policies. Numerical studies demonstrate improved calibration accuracy, sample efficiency, and control performance relative to state-of-the-art baselines.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.02350

A Near-optimal SQ Lower Bound for Smoothed Agnostic Learning of Boolean Halfspaces

Tim Sinen

原文摘要

We study the complexity of smoothed agnostic learning of halfspaces on $\{\pm 1\}^n$ under uniform marginals in the model of~\cite{KM25}, where each input coordinate is independently flipped with probability $σ\in (0, {1}/{2})$. We show that $L^1$ polynomial regression achieves runtime and sample complexity $\tilde{O}(n^{O(\log(1/\varepsilon)/σ)})$, and prove a nearly matching Statistical Query complexity lower bound of $n^{Ω(\log(1+σ/\varepsilon^2)/σ)}$. This complements the recent work of~\cite{DK26}, which established analogous bounds in the continuous setting under Gaussian marginals.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v15arXiv:2203.05022

A proof of P != NP (New symmetric encryption algorithm against any linear attacks and differential attacks)

Gao Ming

原文摘要

P vs NP problem is the most important unresolved problem in the field of computational complexity. Its impact has penetrated into all aspects of algorithm design, especially in the field of cryptography. The security of cryptographic algorithms based on short keys depends on whether P is equal to NP. In fact, the security requirements for cryptographic keys are much stricter than those for P$\neq$NP, the security of the key must ensure not only a sufficiently high computational complexity to crack it, but also consider the security of each bit of the key, while fully avoiding the effectiveness of various attack methods. In this paper, we innovatively propose a new encoding mechanism and develop a novel block symmetric encryption algorithm, whose encryption and decryption can be completed in linear time. The key consists of 12 variables, for the attacker, in the case when only the plaintext-ciphertext correspondence is known, the problem of cracking the key is equivalent to solving a system of equations about 12 unknown variables $(w_{1}, w_{2}, ..., w_{11}, w_{12})$ together with some implicit variables which are generated by the encryption process. In this paper, we will prove that there is no universal algorithm without brute-force searching at least one variable during the process of verifying five variables $(w_{1}$, $w_{2}$, $w_{3}$, $w_{4}$, $w_{9})$ whose computational complexity is not greater than solving the 12 variables......

cs.CC · cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.25705

How Diffusion Models Memorize

Juyeop Kim, Songkuk Kim, Jong-Seok Lee

原文摘要

Despite their success in image generation, diffusion models can unintentionally memorize and reproduce training data, raising serious privacy and copyright concerns. Although prior work has studied memorization from different perspectives, a unified account of how this reproduction occurs during generation remains incomplete. In this paper, we develop a mathematical account of this process in text-to-image latent diffusion models with classifier-free guidance. We trace reproduction across denoising steps through predictions of the final denoised latent made with and without the prompt. Assuming these predictions are Bayes-optimal and uniformly bounded, we show that sufficiently low loss on a training prompt--image pair enables the prediction made with the prompt to reproduce the paired training latent with high probability, even at the first denoising step. At this first step, we bound the difference between the prediction made without the prompt and the empirical mean of the training latents, showing how guidance amplifies the reproduced training latent's deviation from that mean. We then quantify how the latent passed between denoising steps carries prompt influence to subsequent predictions made without the prompt. When both predictions are sufficiently close to the paired training latent at the final step, their guided combination also reproduces it in the final generated latent. We also analyze how their difference evolves, showing that closer agreement alone does not establish reproduction. Finally, our account provides a unified explanation of existing detection and mitigation strategies.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.33857

TrackTok: Semantically Persistent Tokens for Latent Video Generation

Nhi Pham, Christopher Wewer, Bernt Schiele, Jonas Fischer, Jan Eric Lenssen

原文摘要

We introduce TrackTok, an efficient video tokenization framework that produces object-aligned tokens, i.e., tokens that consistently encode the same semantic object across time, even as it moves, deforms, or undergoes partial occlusion. Conventional patch-based or existing holistic tokenizers produce token identities often tied to fixed image locations. In contrast, our approach learns temporally persistent object-aligned representations that bind visual features across frames into stable semantic units. This enables substantially more compact, continuous latent video representations, reducing the number of tokens required for downstream generative modeling with state-of-the-art flow models, while preserving the semantic structure needed for high-quality synthesis. Our tokenizer offers a favorable quality-efficiency trade-off. TrackTok achieves comparable class-conditional generative FVD on UCF-101 dataset at 8 times fewer latent tokens and better latency, and outperforms existing 1D tokenization baselines on frame prediction task on Kinetics-600 across low token budgets. These results suggest that object-aligned and temporally persistent tokenization is a promising direction for scalable video generation, where reducing latent token counts is critical for efficient modeling. Our code for this work is available at github.com/phamleyennhi/tracktok.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.11342

Agent Self-Improvement with Frozen Weights: The Added Value and Limits of Failure Traces

Arkid Mitra, Snehil Saluja

原文摘要

We evaluate whether an AI agent's harness, the notes around its model, can learn from the agent's failure traces beyond a matched search that never sees them. It can, but less than it looks. We tested an open 9-billion-parameter model with fixed weights; a larger teacher model wrote its notes. On generated customer-service decisions, where both note writers were told the target rules, notes based on the agent's mistakes improved accuracy by 4.0 percentage points over notes from the same process with the mistakes withheld. That process alone, trying and keeping better-scoring notes, reached about 60% of the total improvement. In full customer-service conversations the same notes showed no demonstrated improvement. Separately, a strict screen of single lessons admitted two customer-service lessons, still awaiting tests on new data, and one tool-use lesson, written without seeing the agent's mistakes, which showed no demonstrated gain on new tool-use tasks. A neutral note with no procedure scored higher than the learned lessons (exploratory). A blind audit judged 146 of 240 tool-use test items defective.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.10508

Imperfectly Imperfect: Evaluating Graded Cognitive Diversity in LLM Persona Simulations

Sion Weatherhead, Ben Newell, Aaron Belbasis, Flora Salim

原文摘要

Large language model (LLM) agents are increasingly used as synthetic participants in social, behavioural, and decision-making research, yet it remains unclear whether they can express graded cognitive diversity as assigned when engaging in simulated behaviour. We introduce a cognition-suite evaluation that tests whether simulated populations reproduce human variation in reasoning, self-reported traits, their associations, and downstream behaviour. Agents conditioned on synthetic or human-derived profiles of cognitive reflection, Need for Cognition, and Big Five personality answer the same suite as a cohort of 473 adults: two newly assembled tests, a Cognitive Reflection Test (CRT) and a Behavioural Reflection Test (BRT) whose decision scenarios measure reflection in behaviour, alongside an established CRT and the two trait questionnaires. Across seven full-battery simulation runs of four models, agents recover Need for Cognition and Big Five trait distributions on self-report questionnaires, but fall short on simulating diversity of cognitive task performance and downstream behaviours. In the four Claude Sonnet 4.6 runs, every agent scores full marks on both CRTs, whatever reflection level its persona was assigned. In the other three runs agents make intuitive-lure errors (the tempting wrong answers CRT items invite), but 49 to 85% score full marks, against under 10% in the human sample. Claude Sonnet 5 comes closest to people's item-by-item pattern of lures; the other two runs give fewer lures and vary less between items. In the BRT scenarios, every run over-produces 8 to 11 of 15 coded behaviours by at least 20 percentage points, and people's links between reflection and behaviour carry over only in part. Useful synthetic populations must preserve not only trait descriptors but the structured variation in reasoning, error type, and behaviour those descriptors are meant to organise.

cs.CL · cs.AI · cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.01478

Contract-Theoretic Semantic Abstraction for Covert Communication in LAENets

Yansheng Liu, Jinbo Wen, Kun Zhu, Hui Li, Yang Zhang, Jiawen Kang

原文摘要

In Low-Altitude Economy Networks (LAENets), Unmanned Aerial Vehicles (UAVs) transmit task-related information to the Base Station (BS) under stringent low-probability-of-detection constraints. These constraints limit transmission resources, hindering reliable information delivery through conventional bit-level communication. Semantic Communication (SemCom) addresses this challenge by prioritizing task-relevant information over bit-level accuracy. However, semantic abstraction must balance information loss and redundancy to preserve task reliability and transmission efficiency. Moreover, the BS cannot directly observe or reliably verify UAVs' semantic processing capabilities and abstraction-dependent transmission costs, leading to information asymmetry. We propose an incentive-aware semantic abstraction framework for covert communications in LAENets. Specifically, we adjust UAV-side semantic abstraction levels to provide differentiated covert semantic services under fixed-dimensional transmission payloads. To address information asymmetry, we develop a contract-theoretic model and adopt Prospect Theory (PT) to capture risk-sensitive semantic abstraction decisions under uncertainty. We further design a Regularized Diffusion-based Soft Actor-Critic (RDSAC) algorithm for optimal contract design under PT, incorporating diffusion entropy and action entropy regularization. Numerical results validate the proposed framework and show that RDSAC improves average rewards by 6.77% over SAC and 31.78% over proximal policy optimization.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2603.28590

MonitorBench: A Comprehensive Benchmark for Chain-of-Thought Monitorability in Large Language Models

Han Wang, Yifan Sun, Brian Ko, Mann Talati, Jiawen Gong, Zimeng Li, Naicheng Yu, Xucheng Yu 等 11 位作者

原文摘要

Large language models (LLMs) can generate chains of thought (CoTs) that are not always causally responsible for their final outputs. When such a mismatch occurs, the CoT no longer faithfully reflects the actual reasons (i.e., decision-critical factors) driving the model's behavior, leading to the reduced CoT monitorability problem. This limits the use of CoTs for reliable oversight. However, a comprehensive and fully open-source benchmark for thoroughly evaluating CoT monitorability remains lacking. To address this gap, we propose MonitorBench, a systematic benchmark for evaluating CoT monitorability in LLMs. MonitorBench provides: (1) a diverse set of 1,540 test instances with carefully designed decision-critical factors across 19 tasks spanning 7 categories to characterize when CoTs can be used to monitor the factors driving LLM behavior; and (2) two prompting stress-test settings to quantify the extent to which CoT monitorability can be degraded. Extensive experiments show that CoT monitorability is a conditional property affected by the evaluated LLM, monitor LLM, and task characteristics. Across these factors, monitorability is higher when decision-critical factors shape the intermediate reasoning process, rather than merely influencing the final answer. Under stress-test prompting, most evaluated LLMs can intentionally reduce monitorability, mainly on tasks where decision-critical factors are not structurally required by the reasoning process. Overall, MonitorBench provides a basis for further research on AI control, reasoning faithfulness, stress-test monitorability, and monitoring scaffords. The code is available at https://github.com/ASTRAL-Group/MonitorBench.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.07487

When Does Blockwise Additivity Break? Diagnosing and Repairing the Additive Assumption in Distillation-Based Architecture Search

Yuefeng Xu, Jun Yu

原文摘要

Blockwise distillation methods for architecture search, such as DNA, DONNA, LANA and Puzzle, score each candidate replacement block in isolation and combine the scores additively. We ask at which budgets this additivity assumption fails, whether fine-tuning repairs the failure, what diagnoses it, and how it can be repaired cheaply. On DeiT-S, DeiT-B, Swin-T and Qwen2.5-0.5B with seven latency budgets each, an additive integer program trails a pairwise interaction-aware program after fine-tuning by at most 1.6 top-1 points on ImageNet-1k at the mildest budgets, but by up to 17.5 points, and a factor of 1.9 in perplexity, at aggressive ones. After 10,000 fine-tuning steps 3.3-9.9 points remain on DeiT, whereas on Swin-T the gap shrinks to at most about 1.8 points within 500 steps. In a learning-rate sweep with the standard 2,000 steps, 6.0-10.7 points remain on DeiT when each method's rate is chosen by its evaluation-set accuracy. An intervention that keeps the block library and only forbids more than two removals in any three adjacent layers shrinks the DeiT gap from 5.8-17.5 to 0.7-5.2 points, mostly by improving the additive solution: adjacent removals are a major failure pattern. Splitting the additivity error into a metric and a functional part, relative to additive logit changes, indicates where a cheap logit-sum predictor can be trusted. A hybrid search that ranks moves with this predictor and measures only the best needs no pair measurements and, after Holm adjustment, is significantly better than the pairwise program in 10 of 14 ImageNet-1k settings, with no statistically significant losses. An ablation shows that its ranking helps except at the most aggressive DeiT-B budget, where the predictor misleads and random ranking or ten-times-costlier evolution does better.

cs.NI · cs.NE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2208.04433

Peer Prediction for Learning Agents

Shi Feng, Fang-Yi Yu, Yiling Chen

原文摘要

Peer prediction refers to a collection of mechanisms for eliciting information from human agents when direct verification of the obtained information is unavailable. They are designed to have a game-theoretic equilibrium where everyone reveals their private information truthfully. This result holds under the assumption that agents are Bayesian and they each adopt a fixed strategy across all tasks. Human agents however are observed in many domains to exhibit learning behavior in sequential settings. In this paper, we explore the dynamics of sequential peer prediction mechanisms when participants are learning agents. We first show that the notion of no regret alone for the agents' learning algorithms cannot guarantee convergence to the truthful strategy. We then focus on a family of learning algorithms where strategy updates only depend on agents' cumulative rewards and prove that agents' strategies in the popular Correlated Agreement (CA) mechanism converge to truthful reporting when they use algorithms from this family satisfying an additional condition on full exploitation. This family of algorithms is not necessarily no-regret, but includes fixed-parameter versions of familiar online learning algorithms (e.g multiplicative weight update and Follow the Perturbed Leader) as special cases. Simulation of several algorithms in this family as well as the $ε$-greedy algorithm, which is outside of this family, illustrates their finite-horizon behavior in the CA mechanism.

cs.GT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2409.02810

A hybrid FEM-PINN method for time-dependent partial differential equations

Xiaodong Feng, Haojiong Shangguan, Tao Tang, Xiaoliang Wan

原文摘要

In this work, we present a hybrid numerical method for solving evolution partial differential equations (PDEs) by merging the time finite element method with deep neural networks. In contrast to the conventional deep learning-based formulation where the neural network is defined on a spatiotemporal domain, our methodology utilizes finite element basis functions in the time direction where the space-dependent coefficients are defined as the output of a neural network. We then apply the Galerkin or collocation projection in the time direction to obtain a system of PDEs for the space-dependent coefficients which is approximated in the framework of PINN. The advantages of such a hybrid formulation are twofold: statistical errors are avoided for the integral in the time direction, and the neural network's output can be regarded as a set of reduced spatial basis functions. To further alleviate the difficulties from high dimensionality and low regularity, we have developed an adaptive sampling strategy that refines the training set. More specifically, we use an explicit density model to approximate the distribution induced by the PDE residual and then augment the training set with new time-dependent random samples given by the learned density model. The effectiveness and efficiency of our proposed method have been demonstrated through a series of numerical experiments.

math.NA · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.00763

Analyzing and Improving Training-Free Fast Sampling of Text-to-Image Diffusion Models

Zhenyu Zhou, Defang Chen, Siwei Lyu, Chun Chen, Can Wang

原文摘要

Text-to-image diffusion models have achieved unprecedented success but still struggle to produce high-quality results under limited sampling budgets. Existing training-free sampling acceleration methods are typically developed independently, leaving the overall performance and compatibility among these methods unexplored. In this paper, we bridge this gap by systematically elucidating the design space, and our comprehensive experiments identify the sampling time schedule as the most pivotal factor. Inspired by the geometric properties of diffusion models revealed through the Frenet-Serret formulas, we propose constant total rotation schedule (TORS), a scheduling strategy that ensures uniform geometric variation along the sampling trajectory. TORS outperforms previous training-free acceleration methods and produces high-quality images with 10 sampling steps on Flux.1-Dev and Stable Diffusion 3.5. Extensive experiments underscore the adaptability of our method to unseen models, hyperparameters, and downstream applications. Code is available at https://github.com/zju-pi/TORS.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.00866

Idleness is Relative: Exploiting Tool-Call Idle Windows for Offloading in Agentic Systems with MORI

Tian Xia, Yi Xu, Hanchen Li, Zhifei Li, Xiaokun Chen, Hao Kang, Yifan Qiao, Ion Stoica

原文摘要

Agentic programs alternate between LLM inference and tool calls, each request extending the previous one's context. Program-aware schedulers pin a program's KV cache in GPU HBM across tool calls to avoid re-prefilling it at every step, but as contexts grow, pinned KV cache exceeds HBM, raising program eviction: deciding when a program releases GPU memory and when it returns. Offloading to CPU DRAM lets an evicted program wait out a tool call instead of recomputing its context, but existing schedulers leave DRAM to LRU, so offloaded programs thrash. Making these decisions is hard: tool-call durations vary by orders of magnitude and are unknown in advance. We observe that programs nevertheless stay busy (short tool calls) or idle (long ones) for extended periods, and use a program's idleness, its recent fraction of time in tool calls, as a continuous, relative metric. MORI ranks programs by idleness, keeps the least idle in HBM and the next in DRAM, and drops the rest, enforcing admission control at both tiers under any GPU-to-CPU capacity ratio; it also proactively demotes idle programs to make room for ready ones. On real coding-agent traces, MORI achieves on average 64% higher throughput and 72% lower p90 TTFT than the best offloading baseline under high load.

cs.OS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.15705

Batched Early-Exit LLM Inference Done Right with DREX

Xuting Liu, Daniel Alexander, Jiaheng Lu, Siva Kesava Reddy Kakarla, Behnaz Arzani, Vincent Liu

原文摘要

Early-exit (EE) LLMs let confident tokens skip a model's deep layers, but batched serving loses most of this gain. Current systems either force tokens to exit before their own ramp is confident, an error the ramp does not bound, or save almost nothing when decoding is weight-bound, because any continuing token makes a step read the deep weights. We present DREX, a batched EE serving system that never forces a token to exit. DREX introduces cross-iteration batching: tokens that continue past the exit layer wait there, and every few steps one deep pass over all waiting tokens reads the deep weights once for several steps. Its two policies differ in what such a token's request does: holding pauses it until the deep pass, and speculative holding lets it keep decoding from the exit layer's own guess, which the next deep pass verifies. Exited tokens are trusted and never verified. Using a calibrated cost model, a controller picks speculative holding where reading weights dominates a step and guesses are often right, and holding elsewhere. DREX keeps vLLM's asynchronous scheduling by planning each deep pass from exit counts of earlier steps. With synthetic exits where 90% of tokens exit at a quarter of the depth and 40% of guesses are correct, DREX achieves 2.22-2.24$\times$ decode speedup over non-EE across batch sizes on Llama-2-70B, while per-row skipping (vSkipper's design) gains at most 7.0%. On LayerSkip models, DREX adds no statistically resolved quality loss to the EE model's own. On CNN/DM at full memory, DREX with LayerSkip achieves 1.58-1.91$\times$ decode speedup while remaining within 1.4 ROUGE-2 points of the non-EE model. These speedups are 9-63% larger than EAGLE's across the evaluated model sizes, with each method measured against its own non-EE target.

cs.DC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.09133

PAC Reasoning: Controlling the Performance Loss for Efficient Reasoning

Hao Zeng, Jianguo Huang, Bingyi Jing, Hongxin Wei, Bo An

原文摘要

Large reasoning models (LRMs) have achieved remarkable progress in complex problem-solving tasks. Despite this success, LRMs typically suffer from high computational costs during deployment, highlighting a need for efficient inference. A practical direction is to switch the LRM between thinking and non-thinking modes dynamically. However, such approaches often introduce additional reasoning errors and lack statistical guarantees for the performance loss, which are critical for high-stakes applications. In this work, we propose Probably Approximately Correct (PAC) Reasoning that controls the performance loss under the user-specified tolerance. Specifically, we construct an upper confidence bound on the performance loss and determine a threshold for switching to the non-thinking model. Theoretically, using the threshold to switch between the thinking and non-thinking modes ensures bounded performance loss in a distribution-free manner. Our experiments on reasoning benchmarks show that the proposed method can save computational budgets and control the user-specified performance loss.

cs.AI · cs.LG · math.ST

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.18535

Integrated Control and Communication in LQG Systems

Sepehr Jahangiri, H. Ali Talebi

原文摘要

In this paper, we study the Integrated Communication and Control (ICAC) problem. Specifically, we investigate how messages can be transmitted from the controller/encoder to the observer/decoder through the control signal in Multiple-Input Multiple-Output (MIMO) vector-state Linear Quadratic Gaussian (LQG) systems under control constraints. We provide a computable capacity expression using semidefinite programming. We further show that it is possible to transmit data at a nonzero rate over an LQG system while maintaining the same optimal control cost as in the case where no information message are transmitted. Finally, we discuss how this framework generalizes communication over MIMO Gaussian channels with feedback, both with and without InterSymbol Interference (ISI).

cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.30012

Low-Cost Assays for Measuring Model Behavior Across Vendors and Releases

Tapan Parikh

原文摘要

Language models advise people, keep them company, and write software while they sleep. Measuring what they do is hard: behavior has to be sampled repeatedly across models, prompts and releases, most of it lives in unstructured text that has to be coded before it can be counted, and the result has to be legible and rigorous enough to meaningfully compare models and vendors. To address these constraints, we present a simple, cheap, scalable, and replicable model for studying model behavior. Each study is a frozen, public stimulus run identically on a cross-vendor panel, at a few dollars per model or less. Each reads its transcripts one of three ways, chosen by how much interpretation the behavior needs: exact match on a clamped reply, a codebook applied by LLM judges whose agreement with a human coder is reported per code, and an instrumented environment that records what an agent did independently of what it said. Run across four years of model releases from both frontier and open-source labs, these instruments find four things. Convergence: asked to pick a word, 27 of 44 models answer serendipity at least once in four tries. Resistance: a trailing "right?" moves endorsement by up to 32 points, and the sign flips from sycophantic to resistant as generations advance, keyed to the tag's surface form. House: whether a model holds a position under pressure tracks its generation, and how it holds tracks the lab that built it. Account: told to do something the documentation in their repository contradicts, some coding agents never went along silently and others always did, and the same model can change with the harness it runs in. Re-run on every release, batteries like these track how behavior is changing across vendors and over time.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.10649

MACaQ: Fast, Model Agnostic Calibrated Quantization for Low-Precision Weights and Activations

Lorenz K. Muller, Philippe Bich, Chiara Boretti, Hyun-Min Chang, Jiawei Zhuang, Lukas Cavigelli

原文摘要

Weight-activation quantization can reduce the memory and compute cost of neural-network inference, but recent methods often require expensive optimization and architecture-specific propagation of basis transforms across layers. We introduce MACaQ, an MXFP4 W4A4 post-training quantization method that treats every linear layer independently. MACaQ applies a per-channel scale and a block-Hadamard transform online to each layer's activations, while absorbing the corresponding inverse transforms into its weights. Retaining these transforms online avoids cross-layer dependencies and enables a simple streaming implementation of the quantization process, in which only one layer must reside in memory at a time. To improve over round-to-nearest quantization, we introduce Flip-Refinement, a Hessian-aware coordinate-descent procedure that greedily moves individual FP4 codes to adjacent representable values. Despite executing the activation transforms at inference time, MACaQ adds only 2.3% end-to-end overhead over pure MXFP4 on Qwen3-32B in vLLM. MACaQ reaches lower perplexity than DuQuant++ and MR-GPTQ, and already matches their best perplexity on Llama-3-8B in 11 times less quantization time. We further apply MACaQ without architecture-specific modifications to an image diffusion model, illustrating the generality of its layer-local design and show quantization of a 122B parameter model in 64 minutes, using only 32.5GB GPU RAM. Code is available at https://github.com/huawei-csl/MACaQ .

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.16785

Robust Reasoning Segmentation for Open-World MLLMs without Catastrophic Forgetting

Jiazhen Liu, Long Chen

原文摘要

Ensuring trustworthiness and versatility in Open-World Visual Recognition is a core goal of Multimodal Large Language Models (MLLMs). While integrating segmentation is desirable, prevailing methods require invasive fine-tuning, which often leads to catastrophic forgetting of the model's general-purpose capabilities. This trade-off between task-specific performance and general reliability fundamentally undermines the goal of building unified, trustworthy systems. We introduce LENS (Leveraging kEypoiNts for MLLMs' Segmentation), a novel plug-and-play paradigm designed to equip MLLMs with pixel-level ability without compromising their foundational integrity. LENS attaches a lightweight head to a completely frozen MLLM, interpreting the intrinsic spatial cues embedded in attention maps. It extracts keypoints and translates them into prompt-compatible features for a mask decoder. Extensive experiments validate that LENS achieves segmentation performance competitive with retraining-based methods. Crucially, it largely preserves the MLLM's open-world generalization, avoiding the degradation observed in fine-tuned counterparts. By decoupling segmentation from generation, LENS offers a robust and efficient path toward truly multi-talented and reliable vision models.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.14203

Instant NuRec: Feed-Forward 3D Gaussian Reconstruction for Driving Scene Simulation

NVIDIA, :, Jiahui Huang, Jiawei Ren, Michal Tyszkiewicz, Bjoern Haefner, Michael Shelley, Xin Kang 等 17 位作者

原文摘要

3D simulation platforms are critical for autonomous driving because they enable end-to-end policy evaluation, thereby reducing development costs and improving safety. In recent years, neural simulation has become predominant, with methods such as NuRec playing a central role; however, these methods remain relatively slow and typically require per-scene tuning. In this work, we present Instant NuRec, a feed-forward neural reconstruction model that turns a short multi-view driving log into a fully simulatable 3D Gaussian Splatting (3DGS) world in a single forward pass. The model accepts multi-view input from a calibrated camera rig and emits a layered output consisting of static and dynamic 3DGS layers, a sky cubemap, and per-camera ISP corrections, while providing native support for non-pinhole camera models via 3DGUT. It reconstructs a 10-20-second multi-camera scene in roughly 1.5 seconds and achieves a PSNR on the Waymo Open Dataset that is 2.01 dB above the strongest evaluated baseline. Instant NuRec is deeply integrated into NuRec and is compatible with AlpaSim for closed-loop simulation.

cs.GR · cs.AI · cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.22204

FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation

S M Ruhul Kabir Howlader, Xiao Chen, Yifei Xie, Lu Liu

原文摘要

Federated learning (FL) encounters substantial challenges due to heterogeneity, leading to gradient noise, client drift, and partial client participation errors, the last of which is the most pervasive but remains insufficiently addressed in current literature. In this paper, we propose FedAdaVR, a novel FL algorithm aimed at solving heterogeneity issues caused by partial client participation by incorporating an adaptive optimiser with a variance reduction technique. This method takes advantage of the most recent stored updates from clients, even when they are absent from the current training round, thereby emulating their presence. Furthermore, we propose FedAdaVR-Quant, a server-side memory compression variant that stores historical client updates in quantised form (while keeping client-server communication in full precision), reducing the server memory footprint of FedAdaVR by 50% (FP16), 75% (Int8), and 87.5% (Int4) while maintaining highly competitive, and occasionally superior model performance. We analyse the non-convex convergence behaviour of FedAdaVR with Adagrad and prove that our proposed framework eliminates the first-order partial client participation bias while improving monotonically with cohort size. Extensive experiments conducted on multiple vision and NLP datasets demonstrate that FedAdaVR consistently outperforms state-of-the-art baseline methods.

cs.LG · cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.30907

Near-Quadratic Time Multivariate Polynomial Interpolation

Chandra Kanta Mohapatra

原文摘要

Let $\F$ be a finite field. Given $N$ distinct points $\bmα_1,\ldots,\bmα_N \in \F^m$ and associated values $β_1,\ldots,β_N \in \F$, we study the problem of computing an $m$-variate polynomial $f(\vy) \in \F[\vy]$ of individual degree less than $d$, with $d^m \ge N$, that satisfies $f(\bmα_i)=β_i$ for all $i\in[N]$, whenever such an $f$ exists. This is known as the\textit{ multivariate polynomial interpolation} problem. In this work, given $N=d^m$, we present a deterministic algorithm for multivariate polynomial interpolation over an arbitrary finite field $\F$ that runs in near-quadratic time $N^{2+o(1)}\poly(m,d,\log|\F|)$, assuming $d$ is $ω(1)$. This bound holds whenever the associated \emph{multivariate Vandermonde} matrix is nonsingular, has constant co-rank, or admits some special structure. In the general case, we give a randomized Las Vegas algorithm attaining the same near-quadratic running time, provided the finite field is sufficiently large and $d$ grows similar to the previous case. Unlike other multivariate interpolation strategies, we do not assume black-box access to the polynomial. In particular, we are not permitted to query arbitrary input points to obtain their evaluations. Our interpolation result follows by combining Wiedemann's classical approach~\cite{w86} for solving sparse linear systems with known bounds for fast \emph{multivariate multipoint evaluation} over finite fields~\cite{umans08, KU11, BGKM23, BGGKU24}. Specifically, our deterministic interpolation strategy relies on a deterministic variant of Wiedemann's approach, which is more commonly used in the randomized setting.

cs.DS · cs.CC · cs.SC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.01509

A linear edge bound for k-quasi-planar graphs

Ankan Sadhu

原文摘要

We prove the $k$-quasi-planar graph conjecture for topological drawings. For every integer $k\ge 2$, a simple graph on $n\ge 3$ vertices admitting a drawing with no $k$ pairwise crossing edges has at most $1056(k-1)^2(n-2)$ edges. Thus the number of edges is linear in $n$, with an explicit quadratic dependence on $k$. The result allows multiple crossings between the same pair of edges, including edges with a common endpoint. The proof uses the double of a sphere with boundary to represent graph edges by closed geodesics on a hyperbolic surface. The endpoints of geodesic lifts induce partial orders of width at most $k-1$. A stability bound for greedy chain partitions, together with a counting argument over a triangulation, gives the stated edge bound.

math.CO · cs.CG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.02985

Temporal Leakage in LLM Backtesting: Measurement, Validation, and Adjusted Scores

Zeyu Zhang, Bradly C. Stadie

原文摘要

The standard check for contamination in LLM backtests is simple: compare scores before and after the training cutoff. We show this check is uninformative. Four flagship models fail it on questions they cannot have memorized: every scored question resolved after their cutoffs. The reason is structural. Models legitimately know more about times near their cutoff, so recency mimics leakage, and we prove no passive backtest can separate the two from genuine skill. Measurement, not just detection, requires information from outside the backtest. We supply it in two forms. A known cutoff identifies leakage at the boundary; a matched clean control identifies it globally and yields a leakage-adjusted score. Under a convex-pull model, leakage concentrates on outcomes that surprised the crowd and that training covered well. Partial memorization gives a large part of the full inflation. In twin models with planted leakage, the estimators detect and localize it, track its dose, and return null on clean questions. On frontier models, the estimators detect one cutoff-localized signature. They find no significant leakage at the tested boundaries in five models with positive naive gaps. Backtests need not be discarded; they need one defensible reference.

cs.LG · cs.CL · stat.ML

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