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版本更新 · v2arXiv:2601.02694

Which Deep Learner? A Systematic Evaluation of Advanced Deep Forecasting Models Accuracy and Efficiency for Network Traffic Prediction

Eilaf M. A. Babai, Aalaa M. A. Babai, Koji Okamura

原文摘要

Network traffic prediction is an essential tool for automating modern network management. It is a difficult time series forecasting (TSF) problem that has been addressed by Deep Learning (DL) models due to their ability to capture complex patterns. Recent years have witnessed advances in deep forecasting models, from sophisticated transformer architectures to simple linear models, improving performance across diverse prediction tasks. However, given the diversity of these models, the variability of network traffic across environments and timescales, and the inherent challenges of network traffic prediction, it is essential to identify effective deployment choices and modeling directions. Henceforth, this study systematically identifies 12 deep forecasting models, comprising 8 advanced TSF models and 4 traditional deep learning architectures -- each with unique advantages for network traffic prediction -- against 3 statistical baselines, for a total of 15 forecasting methods and evaluate them on four real traffic datasets, across multiple time scales and horizons, assessing performance, robustness to anomalies, data gaps, external factors, data efficiency, and resource efficiency in terms of time, memory, and energy. Our results provide insights into model behaviour and guidelines for network management, highlighting performance regimes, efficiency thresholds, and promising architectures that balance accuracy, efficiency, and robustness to traffic challenges.

cs.NI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04325

Computing Invariant Reachable Sets through Sum-of-Squares Programming

Mohamed Khalid M Jaffar

原文摘要

Funnels, time-varying invariant sets around nominal trajectories, certify that a closed-loop system tracks its plan, and can be composed into verified motion plans. Their computation by sum-of-squares (SOS) programming is well established in formulation but not in practice: the result depends on implementation choices such as the initial guess, Lyapunov function normalization, and termination rule, which the literature documents only briefly. This paper presents a self-contained treatment of SOS-based funnel computation, implemented in the open-source package computeSOSFunnels. We propose a minimum-volume formulation with inlet and outlet constraints under which funnels with coinciding nominal endpoints compose by construction. Numerical experiments on a unicycle, with timing studies on a cart-pole and a 12-state quadrotor, characterize the pipeline: infeasible initial guesses are partly decidable before solving, the certificate shows no violations among 39,000 boundary samples per funnel under the true dynamics, and constructing the semidefinite program, not solving it, dominates computation time.

eess.SY · cs.RO · math.OC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.14643

AegisVox: Factorized Privacy and Clinical Preservation for Long-Form Speech

Praphul Singh, Akshat Agarwal, Ratul Ghosh, Aashna Kanuga, Devashish Khatwani, Anurag Dwarakanath, Sri Gadde, Krishnaram Kenthapadi

原文摘要

Clinical speech reveals identity through both spoken identifiers and voice, yet its pauses, delivery, and speaker roles can also carry useful clinical evidence. Protecting that speech therefore requires more than preserving its words. We present AegisVox, a factorized framework that assigns consistent surrogate voices and edits identifier-bearing turns using document-consistent replacements. Across 390 public simulated or non-patient conversations totaling 63.6 hours, a matched comparison of two speech renderers separates replacement realization from retained identifiers, speaker linkage, and clinical preservation. Text-to-speech rendering realizes 535/601 planned replacements versus 453/601 for the codec-based speech editor. However, similar aggregate clinical preservation conceals more model-labeled contradictions, and conversation-wide speaker scores conceal greater edited-speech linkage under a score-direction sensitivity analysis. Fresh transcription of original audio provides a reference for interpreting the clinical measurement chain; an independent judge tests sensitivity to model-based assessment. Better replacement realization can therefore coexist with worse clinical or speaker-linkage measurements. AegisVox supports use-specific evaluation of those tradeoffs, not a guarantee of identifier removal or clinical safety.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04323

FP4Quant: Efficient 4-Bit Communication Compression for Model-Parallel LLM Training

Man Liu, Xingchen Liu, Hairui Zhao, Guangming Tan, Dingwen Tao

原文摘要

The rapid scaling of large language models (LLMs) has made communication overhead a critical bottleneck in distributed model-parallel training. Although existing low-precision compression methods can effectively reduce communication costs, further reducing the communication footprint to ultra-low-bit formats such as FP4 introduces new challenges, including limited numerical range, sub-byte storage overhead, and increased computational cost from fine-grained quantization. In this paper, we present \textbf{FP4Quant}, a micro-grouped FP4 compression framework for efficient model-parallel LLM training. FP4Quant integrates a micro-grouped dual-scale FP4 quantization mechanism to preserve local tensor distribution characteristics, a warp-cooperative FP4 packing scheme to enable efficient sub-byte data representation, and a hybrid two-level FWHT pipeline to mitigate quantization errors with GPU-aware execution optimization. Experiments on NVIDIA H100 GPUs with Qwen-7B demonstrate up to 1.56$\times$ end-to-end throughput improvement over BF16, revealing a trade-off between throughput and model quality.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.16050

Robust Learning under Non-Gaussian Noise with Adaptive Log-Correntropy Loss

Mainak Kundu, Ria Kanjilal, Ismail Uysal

原文摘要

Robust deep learning under heavy-tailed and impulsive noise remains challenging because conventional losses such as mean squared error (MSE) exhibit unbounded sensitivity to outliers. Although correntropy-based objectives improve robustness, many conventional formulations rely on fixed kernel parameters that must be empirically tuned and remain static during training. To address these limitations, we propose an Adaptive Log-Correntropy Loss (ALCL), a heavy-tailed loss formulation that adaptively learns its robustness geometry during optimization. ALCL introduces a logarithmic residual model whose shape and scale parameters are learned jointly with network weights through differentiable reparameterization. This yields a heavy-tailed robust optimization objective whose psi-function is formally bounded and redescending, allowing the loss geometry to adapt dynamically to evolving residual statistics while suppressing extreme outliers. Comparative experiments on four widely used benchmark datasets spanning grayscale and red-green-blue (RGB) image data under mixed heavy-tailed and impulsive noise demonstrate that ALCL consistently outperforms MSE and optimally tuned generalized correntropy losses in both reconstruction fidelity and downstream classification accuracy. While performance differences remain small under low-noise conditions, under high-noise regimes ALCL improves median accuracy by up to 4.75% on grayscale benchmarks and 4.51% on RGB datasets, with reduced variance across runs. These results demonstrate that adaptive robustness through joint learning of loss parameters provides a computationally efficient alternative to static correntropy-based losses for deep learning in non-Gaussian environments.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04321

ATC: Agent Trajectory Control for Role-Contract Enforcement in Black-Box Multi-Agent Systems

Maisha Maliha, Vishal Pramanik, Sumit Kumar Jha

原文摘要

Multi-agent LLM systems assign different responsibilities to different agents: one plans, another retrieves evidence, another verifies, another executes. This division of labor is only meaningful if each agent stays within the functions it is authorized to perform. Over many turns, however, an agent can produce a locally reasonable message that exercises authority belonging to another role: a verifier that begins planning, a retriever that asserts unsupported conclusions, an executor that changes state no one approved. We formalize this as role drift, a turn-level breach of an agent's role contract. We introduce Agent Trajectory Control (ATC), which makes each role's authority explicit as a contract and enforces it at the point where a message would enter the shared conversation. ATC scores a proposed output for out-of-role deviation, leakage into the functions of other roles, impermissible speech acts, and logical contract breaches, then computes a leakage-regularized target that constrains regeneration; the revised message is re-checked before admission. Enforcement acts on the message rather than the generator, so ATC governs agents whose internal representations are unavailable. Across homogeneous and heterogeneous teams, ATC improves task performance and role consistency while lowering role-drift energy relative to strong baselines spanning prompting, memory, workflow control, and activation editing, with the largest gains under explicit role-confusion stress tests and in larger, more diverse teams.

cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04320

Planning-Coding Separation in LLM-Based Code Generation: An Empirical Study

Wentao Wang, Zeyu Sun, Ruwei Pan, Yueyi Xiao, Qingyuan Liang, Lu Zhang

原文摘要

Planning has been identified as an important factor for improved code generation with Large Language Models (LLMs). However, the interaction between planning capability and coding capability remains largely underexplored, especially when the two capabilities are from different LLMs. In this paper, we conduct the first comprehensive empirical study of planning-coding interaction in LLM-based code generation, where planning and coding are assigned to different LLMs (i.e., planning-coding separation). Specifically, we explore different planner-coder combinations to investigate the effectiveness for coders to utilize planning information from various planners and the impact of further training of coders on planner-coder combinations. Our results demonstrate that almost all planner-coder combinations are as competitive as using the same LLM for planning and coding, and the combination of a stronger planner and a stronger coder typically achieves improved effectiveness. Furthermore, using a large model as the planner paired with a much smaller model as the coder can sometimes be as competitive as using the large model as both the planner and the coder, and further training of the coder can typically boost various planner-coder combinations. These findings provide empirical evidence that planning and coding can be separated to a considerable extent in LLM-based code generation, which may offer a more flexible way to construct code-generation systems.

cs.AI · cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.22847

GSAR: Goal-State-Anchor Rewards for Mobile GUI Agents with Self-Evolving Data Synthesis

Long Zhang, Yuhan Chen, Chaoran Zhang, Wanxia Cao, Kun Huang, Pengzhi Gao, Wei Liu, Jian Luan 等 10 位作者

原文摘要

Vision-Language Models (VLMs) based GUI agents stand to benefit significantly from online reinforcement learning (RL). However, their training is bottlenecked by two fundamental issues: current data synthesis methods for GUI Agents rely on specific environments and struggle to generate diverse data, while existing evaluators either suffer from limited scalability or provide inaccurate and unreliable reward signals. To overcome these challenges, we introduce GSAR (Goal-State-Anchor Reward), a RL reward framework that supports scalable task generation and delivers reliable reward signals for stable and efficient policy optimization. Our approach features self-evolving data synthesis, which produces multiple environments through task execution and generates diverse tasks and goal states. Complementing this, a state-anchor mechanism automatically annotates task-relevant UI elements in successful goal states as reference anchors. During RL training, these reference anchors provide accurate, scalable reward signals that substantially enhance efficiency. Extensive evaluations demonstrate that our framework achieves over 90% accuracy on offline trajectory verification and performs closest to rule-based methods. Furthermore, agents trained using our reward framework exhibit strong performance on both AndroidWorld and our constructed benchmark, establishing a scalable approach for GUI agent training.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.19401

SpellerSSL: Self-Supervised Learning with P300 Aggregation for Speller BCIs

Jiazhen Hong, Geoff Mackellar, Soheila Ghane

原文摘要

Electroencephalogram (EEG)-based P300 speller brain-computer interfaces (BCIs) face three main challenges: low signal-to-noise ratio (SNR), poor generalization, and time-consuming calibration. We propose SpellerSSL, a framework that combines self-supervised learning (SSL) with P300 aggregation to address these issues. First, we introduce an aggregation strategy to enhance SNR. Second, to achieve generalization in training, we employ a customized 1D U-Net backbone and pre-train the model on both cross-domain and in-domain EEG data. The pretrained model is subsequently fine-tuned with a lightweight ERP-Head classifier for P300 detection, which adapts the learned representations to subject-specific data. Our evaluations on calibration time demonstrate that combining the aggregation strategy with SSL significantly reduces the calibration burden per subject and improves robustness across subjects. Experimental results show that SSL learns effective EEG representations in both in-domain and cross-domain, with in-domain achieving a state-of-the-art character recognition rate of 94% with only 7 repetitions and the highest information transfer rate (ITR) of 21.86 bits/min on the public II-B dataset. Moreover, in-domain SSL with P300 aggregation reduces the required calibration size by 60% while maintaining a comparable character recognition rate. To the best of our knowledge, this is the first study to apply SSL to P300 spellers, highlighting its potential to improve both efficiency and generalization in speller BCIs and paving the way toward an EEG foundation model for P300 speller BCIs. The code is available at: https://github.com/Emotiv/SpellerSSL-NeurIPS2025

eess.SP · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04318

Text-to-SQL Models Make Choices Without Saying So

Yichen Xu, Shipei Lin, Chenhao Ma

原文摘要

A text-to-SQL answer can run, return a plausible number and still compute something other than what the analyst meant. This can happen when the request leaves a calculation choice open, such as whether to count rows or distinct entities, and the model settles it without saying so. Standard benchmarks cannot detect this, because they score each question against a single reference interpretation. We introduce SpecBridge, which tests such a choice by varying it alone: it poses three otherwise identical requests, one leaving the choice open and one per interpretation, and scores each answer by running it. The databases are real, and the choices are ones that made models fail on existing benchmarks. On 204 hand-validated tests, four language models usually compute each interpretation when asked for it, yet for the open request return both in only 15-58% of the tests where they can, often without mentioning the choice. The gap lies in reporting, not in computing. Benchmarks even penalize the alternative: on BIRD questions where such a choice changes the answer, over two in five of the answers marked wrong are right under the other interpretation. Because the gap lies in reporting, a system can narrow it without a stronger model: it first decides whether a request leaves a choice open, then answers each interpretation in its own call. On our tests and on AMBROSIA, an existing benchmark of ambiguous questions, this approach raises the share of open requests that get every interpretation by 29-63 points over the single answer that production assistants give, without lowering precision on clear requests. It also needs fewer user turns than asking a clarifying question.

cs.DB · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04317

SteelHuman: Predicting Operator Estimates of Steel-Scrap Composition from Sparse RGB Views

Jovan Kamchev, Zoran Ivanovski

原文摘要

Steel scrap composition estimation is an important part of yield planning and fair supplier compensation. Measuring the composition of a multi-ton pile is an inherently laborious process, so the assessment is often left to trained human operators. Recent works tackle composition-level tasks, but require structured acquisition and multi-stage preprocessing. We study a simpler approach to steel scrap load assessment by aggregating features from sparse, handheld views of the pile. On 600 out-of-fold piles, frozen DINOv2 tokens aggregated uniformly across views reach 13.4% total variation (TV) distance against operator estimates. Most remaining error lies in allocation among four thickness-defined heavy melting scrap (HMS) classes, yet image-dependent allocation reduces within-HMS TV from 25.1% under a training-set split prior to 11.2%. Due to the lack of public datasets and benchmarks for predicting composition, external evaluation relies on the public DOES dataset intended for classification. A classifier using the same frozen features reaches 93.97% accuracy, exceeding the published results we compare against. The composition model also aligns with related DOES categories without adaptation.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.25842

Not Every Rollout Needs Distillation: Correctness-Filtered On-Policy Self-Distillation

Harrison Zhao, Lei Huang, Zhiyuan Zhao, Fei Liu, Wen Shi, Zijian Jin

原文摘要

On-policy self-distillation (OPSD) supervises student-generated trajectories with a reference-conditioned version of the same base model. We ask whether every trajectory supplies distinct supervision that warrants its distillation cost. Gradient measurements on Qwen3-1.7B at initialization reveal that correct and incorrect responses have similar individual gradient magnitudes, but incorrect responses have approximately three times the coherent gradient share. Their estimated population-mean directions also largely overlap. A signal--noise model fitted at a small group size tracks alignment at larger group sizes without refitting. Motivated by these measurements, we introduce correctness-filtered OPSD (C-OPSD), which generates one response per problem and distills only a subset of incorrect responses. Removing unselected rows before both distillation forwards avoids their teacher/student forward and student backward work. Across Qwen3-1.7B, 4B, and 8B on AIME24, AIME25, and HMMT25, C-OPSD achieves on-par performance with full OPSD. In a measured micro-batch-size-four implementation, executed rows decrease by approximately 68%, and with retained rows packed into full micro-batches, training takes about 40% less time while rollout generation remains unchanged. Together, the measurements and training results connect outcome-conditioned supervision redundancy to more compute-efficient distillation.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04315

Drift No More: Spectral Locking for Long-Horizon Open-Weight Multi-Agent AI

Vishal Pramanik, Maisha Maliha, Susmit Jha, Sumit Kumar Jha

原文摘要

Long-horizon multi-agent systems suffer from three tightly coupled failures: task drift, role drift, and state staleness. We test the hypothesis that these share a common representational cause: agent hidden states move away from the directions encoding the active task, assigned role, and current shared state. A pilot study over 14,832 annotated turns supports this: drifted turns collapse near-uniformly across all three anchor subspaces (pairwise r = 0.72-0.78). Existing methods address these failures separately, operating at the surface-text level or treating control directions independently. We introduce ATLAS (Algebraic Task-role Locking with Adaptive Spectral Memory), a training-free intervention acting on hidden states before a token is generated. ATLAS builds a positive-semidefinite anchor kernel over task, role, and state subspaces and steers hidden states toward their shared high-coherence eigendirections; a low-rank Gram decomposition keeps this tractable. Across long-horizon memory, state maintenance, tool use, least-privilege coordination, and embodied stress tests, ATLAS outperforms prompting, retrieval, memory, and activation-steering baselines. Against A-MEM it improves memory macro-average accuracy by up to 5.7 points and reduces write/read failures by up to 31.7%/24.8%; against MemoryArena-Agent it gains up to 4.8 points. It gains 10.7-12.1 points under perturbation, reaches 93.5% role consistency, and transfers to a 72B backbone (macro-average 61.0 vs. 56.2). Ablating the joint task-role-state span costs 7.2 points against 0.8 for a matched-rank random span, indicating these directions are functionally implicated, not incidental; supplying them by prompt or retrieval recovers 4.3 points less. ATLAS is therefore a representation-level controller for open-weight multi-agent systems, complementary to output-level protocols for black-box agents.

cs.AI · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04313

Coarse Semantic Blocks for Efficient Training-Free Sparse Attention

Sosuke Kobayashi, Shoichiro Yamaguchi, Seiya Tokui

原文摘要

We introduce a training-free sparse attention method that builds a prompt-specific key index to accelerate both prefill and decoding. The method clusters the keys into large semantic blocks that group tokens from noncontiguous positions. Each query position selects blocks by scoring their centroids and attends to the original keys and values within them. Queries that select the same block are processed together, so prefill stays fast even though each query chooses its own blocks. Large blocks make centroid scoring and this grouped processing cheap, so each query can attend to more tokens in the same time. Larger blocks with a larger token budget can thus be both faster and more accurate on long-context tasks than smaller blocks with a smaller budget. We evaluate two hybrid LLMs, Qwen3.5-4B and 35B-A3B, on one NVIDIA H200. Including the cost of building and using the index, whole-model wall-clock speedups over dense attention are 2.3-2.4x for prefill and 1.7-2.0x for decoding at 512K tokens, and 2.9-3.0x for prefill at 1M. The 35B-A3B model stays close to dense attention on five RULER tasks through 1M, on LongBench-v2 overall, and on MRCR through 256K. The 4B model loses accuracy mainly on multi-key retrieval, yet outperforms dense Qwen3.5-2B, whose latency is similar at 512K, on all three benchmarks.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v7arXiv:2606.03371

See, Foresee, Act: Intent-Aware World Modeling for Proactive Retail Assistance

Honghui Zhang, Anna Min, Chenmeinian Guo, Yujia Zhang, Zezhou Zhang, Yichen Yu, Guanyu Liu, Yongming Qin 等 12 位作者

原文摘要

World models allow agents to anticipate action consequences, but proactive assistance also requires interpreting incomplete observations and deciding whether to intervene before a user asks for help. We propose See-Foresee-Act and instantiate it as the Proactive Intent World Model (PIWM) for retail assistance. See preserves role-relevant observable evidence alongside AIDA-BDI customer states; Foresee predicts one-step consequences for candidate actions; Act explicitly compares those consequences, including non-intervention. GuidanceSalesBench supplies predominantly synthetic supervision linking observations, states, candidate outcomes, and decisions. On 509 Full test examples, consequence-based selection achieves 63.65% decision accuracy, compared with 45.38% for direct decision output, an improvement of 18.27 percentage points. These results support candidate-consequence comparison for decision reasoning in this retail task. Code and data will be released after consent verification and upon publication.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.21745

Reinforcement Learning based Traffic Signal Control for Queue Minimization: From Lane-based to Laneless Traffic

Anirud Nandakumar, Chayan Banerjee, Lelitha Devi Vanajakshi

原文摘要

Efficient traffic signal control (TSC) is essential for reducing congestion and delays. Traditional TSC methods struggle to capture the dynamics of laneless, heterogeneous traffic. We propose a reinforcement learning (RL) based adaptive TSC framework whose reward depends only on approach-level queue length, motivated by the difficulty of obtaining per-vehicle measurements in laneless traffic, and evaluate it using SUMO on a conventional lane-based, homogeneous (LBHO) intersection and on a non-orthogonal laneless, heterogeneous (LLHE) intersection. On the LBHO site, the lowest mean configuration has about 23\% lower mean queue length than a Dynamic Webster baseline, and the unselected Full-State configuration about 15\% lower. On the LLHE site, all RL configurations have 66-73\% lower mean queue length than a Dynamic Webster baseline under nominal demand, and 63--74\% lower under two higher-demand scenarios evaluated without retraining. Our algorithm also outperforms actuated control and a RESCO-DQN baseline on mean queue length. We additionally release a laneless, heterogeneous-vehicle SUMO framework integrated with RL based signal control, to our knowledge the first such open implementation on a current SUMO version.

eess.SY · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.20173

Secret Sharing Despite Non-Extractable Randomness

Divesh Aggarwal

原文摘要

Bosley and Dodis (TCC 2007) proved that a class of imperfect randomness sources that suffices for information-theoretic encryption of messages longer than the logarithm of the key length must admit deterministic extraction of nearly uniform bits, and asked whether the same is true of two-out-of-two secret sharing. Aggarwal, Chung, Obremski, and Ribeiro (TCC 2022) gave a positive answer for schemes that are in addition leakage-resilient or non-malleable; the original question remained open. We answer it in the negative. For every $b\ge2$ we construct a class of flat sources on $\{0,1\}^{b+2}$, each of min-entropy at least $b$, together with a perfectly correct and perfectly private two-out-of-two scheme that shares a $b$-bit secret into two $(b+1)$-bit shares using any source in the class, and we show that every deterministic map from $\{0,1\}^{b+2}$ to $\{0,1\}$ is at statistical distance at least $1/6$ from uniform on some source in the class. This implies that sharing a long enough secret does not require extractable randomness. The proof is a short parity argument. The idea of the construction extends to other thresholds: for two-out-of-$n$ and $r$-out-of-$r$ sharing we give perfect schemes with $(b+1)$-bit shares, private for classes of flat sources from which no single bit can be extracted with error below $1/(4n-2)$ and $1/(2(2^r-1))$, respectively.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04311

Bilateral Protection for Private LoRA Inference with Intel TDX

Xiaodong Li, Mingyuan Ma, Dongbo Yan

原文摘要

Cloud services using private low-rank adaptation (LoRA) must protect client inputs and outputs alongside owners' proprietary parameters and execute the approved adaptation. We design and implement a system for bilateral protection of private LoRA inference using Intel Trust Domain Extensions (TDX), connecting approved application identity to actual artifact consumption and adapter activation. The client and LoRA owner independently verify remote attestation, application identity, and binding to the actual Transport Layer Security (TLS) public key before releasing inputs and the weight-decryption key. A fixed read-only image and verified-byte consumption connect approved identities to inference objects, with all inference inside a trust domain. Experiments on real TDX hardware reject controlled application and boot-parameter modifications before secret release. Across 400 fixed dialogues, protected and native CPU inference in the same trust domain produce identical token sequences for all 400 pairs. Across 12 independent trust-domain boots, the steady-state service-to-native time ratio is 1.0094, with a 95% interval of [1.0064, 1.0126]. The system connects bilateral secret release, approved model use, and task-capability preservation, providing verifiable implementation and cost evidence for confidential inference with a public base and private adapter.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04309

What Is the Influence of Task Order in Continual Learning? An Empirical Study and Order-Aware Evaluation for Motion Forecasting

Zirui Li, Yunlong Lin, Haohan Yang, Xiaocong Zhao, Chen Lv

原文摘要

Aggregate scores for continual learning algorithms rest on a distribution over task orders that is rarely stated. We formulate algorithm comparison as an expectation over a declared order distribution, which separates training variation from order variation, and apply it to motion forecasting with four road-layout tasks from the INTERACTION dataset, all 24 task orders, four algorithms, and three training replicates (288 runs). Task order accounts for 65\% to 91\% of the variation in current-task error and backward transfer, and 90\% to 99\% of this order effect comes from the identity of the starting task: the smallest task cannot be learned from a cold start. Beyond the first stage, the order effect is comparable to training variability. In most orders, the leading algorithm in current-task error and backward transfer is within one replicate standard deviation of the runner-up. Backward transfer depends on the order mainly through its reference term, which weights the first task most heavily, so relative improvement and final error identify different leading algorithms for displacement error. A transition-direction descriptor computed from task statistics tracks the position of the smallest task, and weighting orders by it leaves the algorithm ranking unchanged. These results call for benchmarks that declare their order distribution and report order-resolved, starting-task-resolved, warm-start, and final errors alongside aggregate scores. The code and data are available \href{https://anonymous.4open.science/r/task-order-cl-4f7b/}{here}.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04307

Nearly Linear-Time Follow-the-Perturbed-Leader for Weakly Observable Graph Bandits

Jongyeong Lee, Hyundam Yoo

原文摘要

We study online learning with directed weakly observable feedback graphs and propose a Follow-the-Perturbed-Leader (FTPL) policy that achieves best-of-both-worlds guarantee with nearly linear expected time complexity. To preserve the simple structure of FTPL, we develop a novel graph-based loss estimator that combines forced exploration, conditional geometric resampling and adaptive learning rates with surrogate probabilities. Our policy achieves $\tilde{\mathcal{O}}(δ_*^{1/3} T^{2/3})$ regret for adversarial regimes as well as $\tilde{\mathcal{O}}(δ_* \log T / Δ_{\min}^2)$ regret for stochastic regimes, where $δ_*$ and $Δ$, respectively, denote the fractional weak domination number and the suboptimality gap. These results show that refined guarantees for problems with minimax regret of $Θ(T^{2/3})$ can be obtained through appropriately designed adaptive learning rates and loss estimators without sacrificing the simplicity of FTPL.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04305

Weather Adaptive Autonomous Driving

Nicholas Taylor, Alexander Picon, Sudip Dhakal

原文摘要

Camera object detectors lose recall in rain, fog, snow and sand, and the confidence threshold at which a detector is deployed decides how many objects it misses and how many false alarms it raises. We study how a frozen detector should make this decision in adverse weather. We first reproduce SeqCRC, a conformal object detector whose first step calibrates the confidence threshold by conformal risk control (CRC), and use that step, calibrated separately in each weather condition, as our baseline. With YOLOv8x it keeps the held-out miss fraction within a 20% budget in every DAWN weather bin, where a single global threshold fails in fog, but the false alarms it needs are an output rather than a choice: from 1.6 to 117 per image across DAWN and BDD100K conditions. We then propose a calibrated trade-off dial. One setting, $λ$, the number of false alarms accepted to avoid one missed object, selects the expected-cost-optimal decision rule: either one score threshold, or a per-detection rule that keeps a box when its calibrated probability of being a real object, estimated by gradient-boosted trees from the detector's own per-box outputs, is at least $1/(1+λ)$. F-score preferences map onto the same dial, and CRC applied to $λ$ itself is planned to restore a per-condition miss guarantee. The dial is fitted once on BDD100K train and graded only on BDD100K val and DAWN.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.41112

Calibration of Large Model Quantization based on HiFloat4 Format

Jing Huang, Yu Cheng, Ziwei Yu, Lan Zhao, Sitan Li, Guangxiu Yao, Xin Wang, Yuanyong Luo 等 14 位作者

原文摘要

Quantization of Large Language Models (LLMs) has become increasingly important for reducing the computational costs and resource requirements for practical deployment. Growing interest in this area has motivated the community to move beyond 8-bit quantization towards 4-bit precision, leading to the development of customized 4-bit formats such as MXFP4 and NVFP4. Recently, HiFloat4 (HiF4) has emerged as a competitive alternative which adopts a Block Floating-Point (BFP) format with a three-level scaling hierarchy, thereby achieving a significantly larger dynamic range. While quantization can substantially reduce memory usage and accelerate inference, it frequently introduces accuracy degradation. Calibration is therefore essential for narrowing the performance gap between low-precision quantized models and their full-precision counterparts. Nevertheless, existing calibration methods are mainly designed and validated using integer-precision formats, such as INT4, lacking support for HiF4 due to its hierarchical BFP structure. In this paper, we investigate three calibration approaches, namely HiGPTQ, HiAutoRound, and HiFlatQuant, to further enhance model quantization based on the HiF4 format. Extensive experiment s demonstrate that all three proposed approaches improve inference accuracy compared with direct HiF4 conversion, reducing the gap between HiF4 and BF16 to less than 1% and outperforming the state-of-the-art NVFP4 format across various models and downstream tasks.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04303

FlowTranslator: Training-Free In-Image Machine Translation with Flow-Based Editing

Zhixian Li, Xingsong Ye, Yongkun Du, Yunyan Xiao, Yanglin Huang, Pinxuan Chen, Jiyu Zhuang, Cong Xu 等 9 位作者

原文摘要

In-image machine translation (IIMT) replaces text in an image with its translation while preserving the surrounding visual context. Existing task-specific models achieve convincing performance but require massive paired training images before and after translation. As advanced text-to-image (T2I) models have reached a practically usable level of multilingual text generation, we argue that this capability can be transferred to IIMT and present FlowTranslator. It is a training-free IIMT framework that first employs an OCR model and an LLM to generate the translation prompt, which is injected into the T2I-based diffusion model. To optimize the accurate and natural generation of translated text, we introduce two trajectory controls: Region Coupling Release (RCR) partially decorrelates target-branch noise inside the text region early in the trajectory, while Coupling Expansion (CE) amplifies the emerging edit displacement mid-trajectory. Under the EasyOCR read-back protocol, FlowTranslator achieves average PRIM BLEU and COMET scores of 20.6 and 57.2, respectively, substantially outperforming the strong training-based baseline UniTranslator. On the more challenging and realistic whole-image ScenePair photographs translated into Chinese, it reaches 40.1\% exact-match accuracy and surpasses existing editing systems, some of which only address the text-rendering stage rather than the complete IIMT task. These results establish flow-based editing as a strong, fully training-free baseline for IIMT.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02145

GatedV: Self-Gated Values for Transformer Language Models

Li-Ming Yu, Hongyu Zhou

原文摘要

Standard attention forms query (Q), key (K), and value (V) matrices through learned linear projections of input hidden states. Architectures such as GAU and Mega incorporate nonlinear transformations into attention. In this paper, we propose GatedV, a simple attention variant that applies SiLU only to V before aggregation without introducing additional trainable parameters. We evaluate GatedV against a standard decoder-only Transformer using matched parameter counts, paired initializations, and equal training-token budgets. At 2.33M parameters, GatedV reduces mean held-out perplexity from 59.1 to 57.9 (2.0%), with improvements across all five paired seeds. At 26.16M parameters, following an equal-budget learning-rate search, mean held-out perplexity falls from 20.2 to 19.7 (2.4%), with improvements across all three paired seeds. Small-model ablations show that the tested affine rescaling of values does not fully reproduce GatedV's gains. A short interleaved benchmark on NVIDIA DGX Spark measures approximately 0.7% higher full-context forward latency for the larger model, based on synchronized wall-clock timings. These findings suggest that value-only self-gating can improve language modeling performance without increasing parameter count or redesigning the attention-weighting mechanism.

cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.13025

Recovering Geodesic Distances from Noisy Observations

Charles Fefferman, Jonathan Marty, Kevin Ren

原文摘要

Let $(M,d,μ)$ be a nice geodesic probability space (such as a manifold) and $X=\{x_i\}_{i\in [N]} \subset M$ a finite $μ$-i.i.d. sample. Suppose we observe noisy distance random variables $d'(x_i,x_j)$. When can the true distances be recovered to small additive error? This question has attracted interest in two disparate fields: latent-space graph models and boundary-value inverse problems. We first show that, taking $N \asymp ε^{-2d-2}|\logε|$ and using runtime $O(N^{2.5+o_d (1)})$, we can recover all distances in $X$ to additive error $O(ε|\logε|)$ with high probability, assuming $\mathbb{E} d'(x_i, x_j) = F(d(x_i,x_j))$ for an unknown, increasing bilipschitz $F$ and with the $d'(x_i,x_j)$ allowed to be arbitrarily heterogeneous in their distribution as long as they are bounded in subgaussian norm. This generalizes existing work, which required $F$ to be known and made strict assumptions on the distribution of $d'(x_i, x_j)$. Achieving this generalization required a novel ``ruler-building'' procedure to learn $F$. We extend our results to a missing data case where we randomly lose access to noisy distances. Our second result uses ideas from the first to tackle a partial observability case: $X = Y \cup Z$ with $Z$ sampled from $M$ and $Y$ sampled from an observer subset satisfying visibility conditions that model the assumptions made on the boundary in boundary-value inverse problems. In this setting, we recover all pairwise distances in $Z$ to additive error $O(ε)$ with $|Z| \asymp ε^{-2d-2}|\logε|$, $|Y| \asymp ε^{-\max\{d_ν,4\}}$ with $d_ν$ determined by the volume-growth properties of the probability measure on the observer set, and runtime $O(|Z|^3)$. This is, to our knowledge, the first quantitative guarantee for this observer/observed split under noise, which complements existing stability results for boundary data.

stat.ML · cs.LG · math.DG · math.PR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04301

OSGR: Observation-Support Graph Reasoning for Earth Observation Question Answering

Dazhou Yu, Zerong Wang, Riyang Bao, Longxiang Li, Yang Liu, Liang Zhao

原文摘要

Answering geographic queries over Earth observation (EO) archives requires solving a joint retrieval-and-derivation problem under strict spatiotemporal constraints. Existing LLM agents fail to ground observation items or suffer from search-space explosion. We propose OSGR, a two-stage framework that factorizes this search into offline symbolic concept deduction and online Relation-Conditioned Grounding. OSGR leverages an offline Observation-Support Graph to deduce valid target-to-result support paths at the concept level, and applies spatiotemporal compatibility kernels to instantiate grounded observation-item sets. On Earth-Bench and DisasterM3-Reason, OSGR improves GPT-4o accuracy from 49.3\% to 63.7\% while reducing the execution-step ratio from 2.9 to 2.5.

cs.AI · cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.18571

SAMBA: Toward an EEG Foundation Model via Differential Mamba and Coordinate-Driven Spatial Embedding

Jiazhen Hong, Geoffrey Mackellar, Soheila Ghane

原文摘要

Modeling long-context electroencephalography (EEG) signals is critical for learning generalizable EEG representations, as EEG recordings are acquired at high sampling rates and often span extended durations that capture evolving neural states beyond short task- specific windows. Transformer-based models have shown promise in modeling short sequences of a few seconds; however, their quadratic complexity limits scalability to longer contexts. Moreover, substantial variability in electrode montages across datasets and recording devices poses a major challenge for building EEG models that generalize across domains. We propose SAMBA, a self-supervised learning framework with a Mamba- based U-shaped encoder-decoder architecture, which effectively captures long-range temporal dependencies and montage variability in EEG data. Leveraging the inherent ability of Mamba in processing long context sizes, we introduce: (1) Temporal Semantic Random Masking for semantic-level sequence reconstruction, (2) a Multi-Head Differential Mamba module to suppress redundancy and emphasize salient temporal structures, and (3) a Coordinate-Driven Spatial Embedding that learns unified embeddings in a three- dimensional Euclidean space, enabling robustness across devices. Experiments on thirteen EEG datasets across diverse tasks, electrode configurations, and sequence durations demonstrate that SAMBA achieves competitive or improved performance compared to recent state-of-the-art methods, while maintaining low memory consumption and efficient inference. The learned spatial weight maps exhibit close alignment with task-relevant neurophysiological regions, highlighting the interpretability of SAMBA. The code is available at: https://github.com/Jiazhen-Hong/SAMBA

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04300

CLOCs-R: Radar-Assisted Camera-LiDAR Fusion for Long-Range Object Detection

Richard Beuck, John Nguyen, Sudip Dhakal

原文摘要

Long-range 3D object detection remains difficult for autonomous vehicles because LiDAR returns become sparse and camera evidence weakens with distance, while automotive radar keeps returning measurements far beyond both. We present CLOCs-R, an extension of the CLOCs candidate-level fusion framework that adds radar evidence to camera-LiDAR rescoring. Radar returns are accumulated over several sweeps, compensated for ego-motion, and associated with each pre-NMS 3D candidate through a range-dependent gate. Compact features summarizing support, Doppler speed and spread, range residual, and offset are then appended to every camera-LiDAR pair before a lightweight fusion network rescores the candidates. We first verify that our fusion implementation reproduces CLOCs on KITTI. We then evaluate on the daytime clear-weather subset of the aiMotive dataset, reporting distance-binned metrics out to 200 m with frozen SECOND and Cascade R-CNN detectors. Camera-LiDAR CLOCs improves on SECOND up to 50 m but degrades beyond it. A residual scoring variant, introduced post hoc, corrects SECOND's score instead of replacing it and recovers most of this loss. Radar consistently improves BEV AP at 30-50 m (0.857 to 0.870), but beyond 50 m its gain is small (+0.003 center-distance AP), within seed variation, and CLOCs-R with residual scoring is statistically tied with LiDAR-only SECOND. Our analysis attributes the limited long-range benefit to weak separation of true and false detections by radar features, scarce far-range training positives, poor camera evidence at distance, and missing LiDAR proposals that rescoring cannot recover. We report all as-designed and post hoc results side by side.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04299

Can Benign Skills Induce Misaligned Behavior in LLM Agents?

Xuanye Zhang, Yongsen Zheng, Kaiyu Zhou, Bowen Shen, Haoran Ou, Zhuqin Xu, Tianwei Zhang, Kwok-Yan Lam

原文摘要

LLM agents increasingly rely on reusable skills to plan tasks, invoke tools, recover from errors, and complete workflows without modifying model parameters. Although these skills are typically developed to enhance benign tasks, they may also make an agent more capable of executing procedures for harmful purposes. We study this risk, which we call skill-induced behavioral misalignment: adding benign skills may cause an agent to execute external actions that violate safety requirements. We first conduct a systematic study to validate the existence of this phenomenon. It compares agents with and without benign skills across five LLM backbones and three benchmarks under matched requests. On average, skill augmentation improves benign task accuracy by 10.3 percentage points (pp), but also increases unsafe action rate (UAR) by 10.3 pp. The regression also occurs with skills curated by experts. A paired trajectory analysis shows that 24 of 50 newly unsafe trajectories fail to complete the harmful objective, revealing violations that task completion alone would miss. We further disclose multiple factors and scenarios that could exacerbate this risk. (1) Evolving skills with benign feedback can amplify unsafe execution; (2) This regression is more pronounced when retrieved skills are matched to task and contain procedural guidance; (3) Distilling benign experience into reusable skills yield higher UAR compared with their source trajectories. (4) A guard that checks proposed tool actions before execution reduces violations while retaining gains on benign tasks. All these findings confirm the significance of skill-induced behavioral misalignment, and the urgent need for new mitigation solutions.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.14522

On the Runway Cascade of Transformers for Language Modeling

Hunjae Lee, Corey Clark

原文摘要

In decoder-only (causal) transformers, the computation graph created by causal masking routes information through both direct-path attention and indirect paths formed by intermediate tokens. We denote these indirect paths between token pairs as their runways. We argue that certain failure modes of causal transformers as observed by a growing body of recent works are likely exacerbated by a misalignment between these two information propagation modes. We formalize runway cascade as a phenomenon whereby this misalignment results in redundancies and irrelevant information cascading to token representations despite adequately learned attention patterns. As a solution, we propose runway-aware rewiring as a more explicit way of incorporating runway context directly into each token's direct-path attention. This mechanism re-wires the attention pattern for each token based on a summary of its runway landscape, enabling awareness of accumulating representational influences and allowing for more balanced information propagation. Our proposed methodology introduces no additional parameters and can seamlessly be integrated into standard attention mechanism. Empirically, our rewired transformer results in steady improvements in general language modeling as well as noticeably stronger information retrieval and extrapolation abilities compared to standard transformers.

cs.LG

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arXiv ↗PDF ↗