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新论文 · v1arXiv:2611.16653

Information Spectrum Methods for Multiple-Access Channels --Part II : $\varepsilon$-Capacity Regions in the Mixed Finite-Alphabet Case

Te Sun Han, Hideki Yagi

原文摘要

In a nonergodic channel such as a slowly fading link, the channel law is drawn once and held fixed for the whole codeword, and the relevant performance measure is the $\varepsilon$-capacity. We study the $\varepsilon$-capacity regions of two-user mixed multiple-access channels (MACs) with finite alphabets, additive cost constraints and an arbitrary mixing measure. The direct parts are derived from a single information spectrum formula; the converse parts use, in addition, Fano's inequality and Ahlswede's strong converse applied to each component. Without knowledge of the realized component, we prove a single-letter inner bound for every $0\le\varepsilon<1$, in which one input serves all components; for $\varepsilon=0$ it is tight, and the capacity region is given by the essential infima of the component mutual informations. We prove a single-letter outer bound, the outage region, in which each component may use its own input; it holds with or without channel state information (CSI) at either terminal, and it is the $\varepsilon$-capacity region when the encoders know the component and the parameter space is countable. Both bounds coincide, in all four CSI scenarios, for well-ordered mixed MACs, which include degradedly ordered families and mixtures with a common optimal input, such as mixed additive MACs. Without CSI the outer bound can be strictly loose, and the no-CSI $\varepsilon$-capacity region for $0<\varepsilon<1$ remains open in general. Finally, we extend Ahlswede's strong converse to $K$ users with cost constraints and show that most of these results extend to $K$-user mixed MACs.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16327

Designing Information in a Forest: Algorithmic Aspects

Konstantin Zabarnyi

原文摘要

The Bayesian persuasion framework models strategic information revelation by a sender to one or more receivers. We study an intricate version of the multi-receiver persuasion setting in which the communication hierarchy is a forest - i.e., some receivers observe more informative signals than others, and the partial ordering of the receivers according to the information they get is represented by a pre-specified directed forest. This model naturally captures interactions in hierarchical organizations, and it lies in between the two most commonly studied versions of Bayesian persuasion: private persuasion and public persuasion. Inspired by the classic model of private persuasion, we focus on a setting with a binary state of nature and binary-action heterogeneous receivers s.t. the sender's utility is a monotone function of the set of receivers taking a specific desirable action. We extend the computational tractability result for symmetric sender's utility from private persuasion to the forest hierarchy framework using methodology inspired by optimal transport theory. We further provide a polynomial-time algorithm when the sender's utility is modular. On the negative side, we show that, unlike in private persuasion, the case of a supermodular sender's utility function leads to an NP-hard optimization problem which further does not admit an FPTAS unless P=NP; this result holds even when each tree component of the forest is a chain of 3 receivers. To complement the hardness result, we provide a simple approximation algorithm with the approximation ratio depending linearly on the depth of the forest hierarchy. We further provide an analogous algorithm for the submodular utility case. Finally, beyond forest communication hierarchies, we rule out an FPTAS for the sender's optimal utility even in the symmetric utility case, unless P=NP.

cs.GT · econ.TH

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2508.18787

Design, Implementation and Evaluation of a Real-Time Remote Photoplethysmography (rPPG) Acquisition System for Non-Invasive Vital Sign Monitoring

Constantino Álvarez Casado, Sasan Sharifipour, Manuel Lage Cañellas, Nhi Nguyen, Le Nguyen, Miguel Bordallo López

原文摘要

The growing integration of smart environments and low-power computing devices, coupled with mass-market sensor technologies, is driving advancements in remote and non-contact-based physiological monitoring. However, deploying these systems in real-time on resource-constrained platforms introduces significant challenges related to scalability, interoperability, and performance. This paper presents a computationally efficient real-time remote photoplethysmography (rPPG) system, designed to extract physiological signals, such as heart rate, respiratory rate, and oxygen saturation (SpO2), from facial video streams. The system is built on the Face2PPG pipeline, which processes video frames sequentially for rPPG signal extraction and analysis, while leveraging a multithreaded architecture to manage video capture, real-time processing, network communication, and graphical user interface updates concurrently. This design targets continuous operation at 30 frames per second (FPS), with adaptive feedback through a collaborative user interface to guide optimal signal capture conditions. The network interface includes both an HTTP server for continuous video streaming and a RESTful API for on-demand vital sign retrieval. To ensure accurate performance despite limited computational resources, we use a hybrid programming model combining Functional Reactive Programming (FRP) and the Actor Model, allowing event-driven processing and efficient task parallelization. We evaluate HR accuracy on three public datasets (COHFACE, LGI-PPGI, and UBFC-RPPG), and conduct preliminary evaluations of RR on COHFACE and SpO2 on PURE. We also assess the processing time and end-to-end operation of the system on two computers, including live HR and SpO2 agreement with a medical-grade fingertip pulse oximeter across five 10-minute sessions. Our work addresses key challenges in real-time contactless biosignal monitoring.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02275

Data-Scarce, Multi-Granular, Multimodal: Benchmark Design for Biosynthetic Gene Clusters

Hanlin Xiao, Eriko Takano, Mauricio A. Álvarez, Rainer Breitling

原文摘要

Biosynthetic gene clusters (BGCs) are co-located genes that encode the biosynthesis of specialized metabolites, an important source of antibiotics, anticancer agents, immunosuppressants, and agricultural bioactives. Deep learning, particularly self-supervised foundation models, has spurred interest in BGC sequence modeling, but evaluation has lagged behind: existing benchmarks often rely on coarse classification over small curated datasets, providing limited discrimination among models and insight into downstream utility. We propose a benchmark-design framework in which an informative benchmark should be discriminative, separating models with statistical confidence, and applicable, spanning scientifically meaningful tasks under different levels of generalization. We instantiate this framework as BGC-Bench, which derives five tasks from MIBiG, ranging from A-domain substrate prediction to BGC-molecule retrieval, evaluates them under genotype- and phenotype-shift splits, includes an active-learning setting for label-scarce discovery, and is easily extensible to new tasks and encoders. BGCs further provide a shared testbed across granularities and modalities: BGC-Bench compares twelve encoders on nucleotide, amino-acid, or Pfam-domain sequences, including compositional baselines for each input type, and combines BGC and chemical encoders for cross-modal retrieval. No single representation dominates: the best granularity depends on the biological scale of the task, and pretrained encoders do not consistently outperform other representations across tasks and splits. Active-learning experiments show that most tasks remain data-limited, encoder rankings change with labeling budget, and acquisition strategies yield modest gains. These results show that BGC model evaluation requires task-diverse, shift-aware benchmarks, and the framework can be generalized to benchmark design beyond BGCs.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.01502

Conformal Risk Control under Non-Monotone Losses: Theory and Finite-Sample Guarantees

Tareq Aldirawi, Yun Li, Wenge Guo

原文摘要

Conformal risk control (CRC) provides distribution-free guarantees for controlling the expected loss at a user-specified level. Existing theory typically assumes that the loss decreases monotonically with a tuning parameter that governs the size of the prediction set. However, this assumption is often violated in practice, where losses may behave non-monotonically due to competing objectives such as coverage and efficiency. In this paper, we study CRC under non-monotone loss functions when the tuning parameter is selected from a finite grid, a setting commonly arising in thresholding and discretized decision rules. Revisiting a known counterexample, we show that the validity of CRC without monotonicity depends critically on the relationship between the calibration sample size and the grid resolution. In particular, reliable risk control can still be achieved when the calibration sample is sufficiently large relative to the grid size. We establish a finite-sample guarantee for bounded losses over a grid of size $m$, showing that the excess risk above the target level $α$ scales on the order of $\sqrt{\log(m)/n}$, where $n$ is the calibration sample size. A matching lower bound demonstrates that this rate is sharp for the CRC selection rule under the same population-safe endpoint condition. We also derive refined guarantees under additional structural conditions, including Lipschitz continuity and monotonicity, and extend the analysis to settings with distribution shift via importance weighting. Numerical experiments on synthetic multilabel classification and real object detection data illustrate the practical implications of non-monotonicity. Methods that explicitly account for finite-sample uncertainty achieve more stable risk control than approaches based on monotonicity transformations, while maintaining competitive prediction set sizes.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.21622

On the Origin of Algorithmic Progress in AI: Evidence from Language Model Pre-Training

Hans Gundlach, Alex Fogelson, Jayson Lynch, Ana Trisovic, Jonathan Rosenfeld, Anmol Sandhu, Neil Thompson

原文摘要

Algorithms have been estimated to increase AI pretraining FLOP efficiency by a factor of 22,000 between 2012 and 2023 (Ho et al., 2024). Running ablation experiments on key innovations from this time period, we are able to account for less than 10x of these gains at small compute scales. Surveying the broader literature, we estimate that additional innovations not included in our ablations also account for less than 10x, yielding less than 100x efficiency gains total in low-compute regimes. This leads us to conduct scaling experiments, which provide evidence that the efficiency gap can be explained by algorithms with scale-dependent efficiency improvements. In particular, we conduct scaling experiments between LSTMs and Transformers, finding evidence for exponent differences in their compute-optimal scaling law while finding little scaling difference for many other innovations. These experiments demonstrate that, contrary to standard assumptions, an algorithm's efficiency gains are tied to compute scale. Using experimental scaling extrapolations and literature estimates, we can account for the majority of efficiency gains over the same time period. Our results indicate that algorithmic progress at smaller training compute levels has been far slower than previously assumed, and that measures of algorithmic efficiency are strongly reference-dependent.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.22838

How Many Votes is a Lie Worth? Measuring Strategyproofness through Resource Augmentation

Ratip Emin Berker, Vincent Conitzer, Eden Hartman, Jiayuan Liu, Caspar Oesterheld

原文摘要

It is well known, by the Gibbard-Satterthwaite Theorem, that when there are more than two candidates, any non-dictatorial voting rule can be manipulated by untruthful voters. But how much stronger is an untruthful voter than a truthful one under different voting rules? We suggest measuring the potential comparative advantage of a strategic voter by asking how many copies of their (truthful) vote must be added to the election in order to achieve an outcome as good as their best manipulation. Intuitively, this definition quantifies what a voter can gain by manipulating in comparison to what they would have gained by finding like-minded voters to join the election (equivalently, by increasing their voter weight). Our measure is prior-free and requires no assumptions on the voters' cardinal utilities. Using this framework, we obtain a principled method to compare the manipulation leverage for different voting rules. We show that while the manipulation leverage of many rules grows linearly with the number of voters, Borda Count's is m-2, where m is the number of candidates. We prove that the positional scoring rule with the smallest manipulation leverage is always Borda Count (if voters outnumber candidates) or Plurality (otherwise). Further, any rule satisfying a weak form of majority consistency (and thus any Condorcet extension) cannot outperform Plurality, while any majoritarian rule will perform significantly worse. Consequently, Borda Count has much lower manipulation leverage than these wide classes of rules under large electorates. Finally, we establish a quantitative extension of the Gibbard--Satterthwaite theorem, proving no anonymous and onto voting rule can have manipulation leverage that is lower than m-2 across all electorate sizes. As a result, Borda Count is indeed optimal among all such rules.

cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13134

A Fast Algorithm for Subspace Convolution

Shlomo A. Perles, Omri Weinstein

原文摘要

A cornerstone result in Boolean lattice theory is the subset-zeta transform, which computes the $f$-weighted subset summation problem $(Z f)(B)=\sum_{A\subseteq B}f(A)$ for all $ B\subseteq[n]$ in $O(N \log N)$ additions instead of the naïve $O(3^n) = O(N^{\log_2 3})$, where $N=2^n$. This paper studies the algebraic analogue of this problem--the subspace summation problem--where subsets are replaced by linear subspaces in $\mathbb{F}_q^n$. Surprisingly, we show that this problem admits a similar $Θ_q(N_q \log N_q)$-time algorithm, where $N_q$ is the number of subspaces. This improves substantially (in both theory and practice) over lattice-based algorithms ([BHK+16, KKW16]), which are known to be optimal for addition-only algorithms over $\mathbb R$. We prove similar runtimes for inversion, rank-constrained sums and span convolution. We describe two main applications: Fast linear algebra on Grassmann graphs: Matrix--vector multiplication for arbitrary distance-invariant kernels on $N$-vertex Grassmann graphs in $O_q(N\log N)$ arithmetic operations, together with fast spectral projections and exact solutions of the corresponding linear systems. Graph coloring of low binary cut-rank graphs: An $n^{2^{r^2/4+O(1)}}$-time algorithm for computing the chromatic number and chromatic polynomial of an $n$-vertex graph, given a rank-decomposition of width $r$. We also give quantitative reductions from satisfiability and monotone constraint problems. Experiments show speedups in measured running time of up to $18$ over the covering-pair algorithm for the zeta transform, $190$ over Poisson summation for rank-constrained sums, and $22$ over the distance-matrix recurrence for Grassmann kernel multiplication. Lean 4 formalizations of the transform identities, arithmetic operation bounds, and graph-coloring constructions accompany the paper [PW26].

cs.DS · cs.CC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12529

Perceived Agency During Agent-Avatar Control Transitions in Virtual Reality

Jingyi Zhang, Anthony Steed

原文摘要

Prior beliefs about whether a virtual human (VH) is controlled by a human or a computer can shape how users interpret and respond to its behaviour. As emerging hybrid VH systems allow control to transfer between human operators and computer programs during an ongoing interaction, it remains unclear whether perceived agency updates with the actual controller or continues to reflect users' prior beliefs and first impressions. We investigated this question in a within-subject VR study with 24 participants across four conditions: always computer-controlled, computer-to-human, human-to-computer, and always human-controlled. We designed an interaction task in which participants acted as interviewers, asking the VHs a fixed set of questions. Before each interaction, participants were informed whether the VH was human- or computer-controlled, matching the actual controller at the beginning, but were unaware of possible control transitions. We found that perceived agency was strongly influenced by the controller presented at the beginning of the interaction, with no evidence of updating following the control transitions. Social evaluations including likeability and co-presence were generally higher in conditions where human control occurred later and covered a greater proportion of the interaction. The results suggest that prior beliefs and first impressions continued to shape perceived agency, whereas social evaluations were more sensitive to the VH's actual behaviour over the interaction.

cs.HC · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12332

Do Machine Learning Models Predict?

Michele Pizzochero, Emanuele Ratti

原文摘要

Machine learning (ML) models are said to predict. Philosophers draw a line between accommodation and prediction, depending on whether or not the evidence a theory succeeds on was used in constructing it. Does this distinction survive when a theory is replaced by an ML model? We argue so, yet the dividing line becomes fuzzy. Whether an output of an ML model counts as an accommodation or as a prediction hinges upon the relationship between the patterns the input instantiate and those on which the model was selected through its training. Once this relation is taken into account, most successful outputs of ML models, we suggest, bear a strong accommodation character. We further trace the consequences of our account for confirmation, novelty, and scientific realism.

cs.CY · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.35110

Understanding and Repairing Cooperative Perception under Adverse Weather

Mengxu Ma, Linshan Yan, Kailong Zhang, Ruiyang Guo, Dagang Shen, Yichen Fan, Hongping Gan

原文摘要

A cooperative detector can lose a correct vehicle detection when surrounding LiDAR measurements change, even if the returns on the vehicle remain unchanged. We study this failure as Weather-Induced Context Interference. On our CoInfra development set, controlled removal outside annotated object boxes turns 22.1% of V2X-ViT's previously correct detections into misses. Restoring network features around the objects to their values before removal recovers 98.6% of these misses. The same restoration in each of three equally sized regions excluding the objects recovers none. Candidate analysis finds accurate boxes that are not selected, while other targets lack well-localized candidates. With paired reference features unavailable at inference, Adverse-Weather Cooperative Repair (AWCR) repairs candidates from current observations. A lightweight Local Correction head generates bounded alternatives, and Quality Selection ranks them alongside original boxes with a correction weight. In V2X-R simulation, AWCR also filters peer returns before detection using Point Classifier trained on generated-point labels. Candidate repair improves mean car BEV AP@0.7 by 4.22 points across three fits on natural adverse-weather CoInfra development inputs and vehicle AP@0.7 by 5.00 points on a rain-affected public recording. It also improves released HEAL, FadeLead, and V2X-DGW detectors with their pretrained weights fixed. On the full V2X-Radar-C validation benchmark, HEAL with AWCR achieves state-of-the-art LiDAR-only cooperative detection performance of 68.22 vehicle BEV AP@0.7.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2504.17410

Bias-Eliminated PnP for Stereo Visual Odometry: Provably Consistent and Large-Scale Localization

Guangyang Zeng, Yuan Shen, Ziyang Hong, Yuze Hong, Viorela Ila, Guodong Shi, Junfeng Wu

原文摘要

In this paper, we first present a bias-eliminated weighted (Bias-Eli-W) perspective-n-point (PnP) estimator for stereo visual odometry (VO) with provable consistency. Specifically, leveraging statistical theory, we develop an asymptotically unbiased and $\sqrt {n}$-consistent PnP estimator that accounts for varying 3D triangulation uncertainties, ensuring that the relative pose estimate converges to the ground truth as the number of features increases. Next, on the stereo VO pipeline side, we propose a framework that continuously triangulates contemporary features for tracking new frames, effectively decoupling temporal dependencies between pose and 3D point errors. We integrate the Bias-Eli-W PnP estimator into the proposed stereo VO pipeline, creating a synergistic effect that enhances the suppression of pose estimation errors. We validate the performance of our method on the KITTI and Oxford RobotCar datasets. Experimental results demonstrate that our method: 1) achieves significant improvements in both relative pose error and absolute trajectory error in large-scale environments; 2) provides reliable localization under erratic and unpredictable robot motions. The successful implementation of the Bias-Eli-W PnP in stereo VO indicates the importance of information screening in robotic estimation tasks with high-uncertainty measurements, shedding light on diverse applications where PnP is a key ingredient.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11981

Thermo-VLA: A Thermal-Aware Vision-Language-Action Model with Gated Residual Modulation for Robotic Manipulation

Jingxuan Chen, Junshi Luo, Chencheng Dong, Xiaojian Nie, Tongge Huang, Qiang Huang

原文摘要

Recent Vision-Language-Action (VLA) models have begun incorporating thermal sensing, but existing approaches typically treat thermal images as ordinary visual inputs, providing limited support for temperature-aware relational reasoning in manipulation. To address this limitation, we propose Thermo-VLA with a ThermoRes Encoder, which decomposes thermal images into cold and hot attribute representations and injects them into the RGB token through language-conditioned residual modulation, enabling temperature-driven target selection and continuous manipulation. We further construct six real-world robotic tasks on a dual-arm Unitree G1 platform, spanning basic grounding to relational reasoning. Experiments show that Thermo-VLA achieves an average success rate of 0.86 across four thermal-dependent tasks, outperforming a pi_0.5 baseline that directly incorporates RGB-thermal observations by 18 percentage points. Under unseen-object and scene variations, Thermo-VLA maintains an average success rate of 0.78, achieving the best generalisation performance among the compared methods. Beyond thermal sensing, we transfer the same residual modulation mechanism to colour attributes and observe consistent improvements in both simulation and real-robot experiments, indicating its potential to generalise beyond thermal attributes. Project website: https://j-x-chen.github.io/Thermo-VLA-web/

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.01462

An Enigma of Artificial Reason: Investigating the Production-Evaluation Gap in Large Reasoning Models

Mingzhong Sun, Teresa Yeo, Armando Solar-Lezama, Tan Zhi-Xuan

原文摘要

Studies of human reasoning have shown that people are typically stronger at evaluating reasoning than producing it from scratch. In contrast, large reasoning models (LRMs) are trained to excel at producing long chains of reasoning to solve complex problems. How then do LRMs perform at evaluating reasons? We investigate this with the Valid-Answer-Invalid-Reasoning (VAIR) dataset: math problems and solutions with trivial reasoning flaws but valid answers, designed to isolate reasoning evaluation from the confound of reasoning production. Unlike humans, who we find are only 6% worse at grading than solving such problems, we find a substantial production-evaluation gap in LRMs: frontier models score as low as 52% when evaluating VAIR solutions, despite near-perfect solution production. Why this enigma? Through chain-of-thought (CoT) analysis, we find evidence of an answer confirmation bias: LRMs often produce then check for the correct answer instead of carefully verifying each step, fabricating rationalizations even when noticing anomalous reasoning. Linear probes corroborate this, showing that while LRM activations encode some representation of valid reasoning, they fail to robustly represent VAIR solutions as invalid. Causal patching of the final answer's representations causes LRM verdicts and activations to flip at substantial rates, demonstrating that answer validity is a causal factor in models' confirmation biases. These findings indicate an outstanding limitation in dominant approaches to reasoning training, which incentivize LRMs to produce and confirm reasoning towards correct answers, but not to robustly evaluate the underlying reasons

cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11436

Retrospective Analysis of Team-Following Trading Profitability in Pumpfun Meme Coins Using Funding Networks

Fail Gafarov

原文摘要

If the right token-creation teams had been known, how much could following them have returned, and what is left when only information available before each launch is used? We study ten separately recorded Pumpfun windows (April 2024 to September 2026), each reconstructed independently from real-SOL reserve states and finite-capital accounts that start with one SOL. Teams are directed funding groups ranked in hindsight. When every team exits at its own best reserve threshold, chosen in hindsight, the main account ends above one SOL in all 8 windows that have a qualifying team (1.10 to 11.32 SOL), but free cash exceeds one SOL in only 5, and marks of positions that never reached their threshold make up 17--94% of equity. If teams are recognised online, from tokens whose first hour has completed, 9 of 19 teams become identifiable only after their last launch; the remaining 10 end above one SOL in 10 cases, with free cash above one SOL in 7; funding links in this step still come from the full window, so even these results are not a fully realisable signal. A large model balance, a result above random cohorts that repeat the threshold search, and a statistical rejection in the selection-matched nulls (3 of 40 tests after Holm adjustment) are three different statements, and these nulls condition on the discovered funding graph, so they do not calibrate the whole selection procedure. Midpoint tests describe a further source of apparent opportunity. A detailed analysis of the best and the worst window, with every analysis parameter varied, shows that the worst result, which is close to break-even, rises several-fold with a stop rule or another funding-graph construction. The results are conditional retrospective model accounts, not executable profits, and not evidence of a causal effect of the SOL price.

physics.soc-ph · cs.SI · q-fin.TR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10857

Polynomial Denoisers on the Image Manifold: Volterra Networks for Self-Contained Pixel Diffusion

Siddharth Roheda, Rohit Chowdhury, Amit Satish Unde, Aniruddha Bala

原文摘要

Natural images are often conjectured to lie on a low-dimensional manifold within the high-dimensional pixel space. Recent work leverages this to build self-contained pixel-space diffusion models without specialized machinery or pretrained components. A plain Transformer on raw image patches suffices, provided it predicts the clean image rather than the noise and compresses each patch through an aggressive bottleneck. The mixing operator, however, has remained softmax attention, inherited from sequence modeling rather than derived from denoising. For Gaussian noise, the optimal denoiser admits a canonical expansion in polynomial functionals of the observation, the Wiener--Volterra series. We prove that softmax attention, and any normalized mixing rule (including linear attention), cannot realize any polynomial map beyond an affine one. In contrast, Volterra layers realize this class directly and, on a data manifold, achieve approximation rates governed by the manifold dimension rather than the ambient pixel dimension. We therefore propose the architecture these results prescribe; raw image patches passed through a bottleneck embedding, followed by cascaded low-rank Volterra layers, which carry no activation function because each layer is nonlinear in its own right. To our knowledge this is the first pixel-space diffusion architecture whose mixing operators are Volterra layers throughout, with no attention, no convolution, no pretrained components, and no activation function on the residual path. On ImageNet $256 \times 256$ and $512 \times 512$, the resulting model is competitive with self-contained pixel-space diffusion at matched parameter and FLOP budgets.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10699

PLS-GS: Probabilistic Lidar Scaffolds for Compact, Surface-Aligned Gaussian Splatting

Krishnan Harikumar, Alejandro Fontan, Thierry Peynot, Emili Hernández, Niko Sünderhauf

原文摘要

While 3D Gaussian Splatting (3DGS) is typically optimized from images only, many robotics and mapping applications provide both camera and high-definition LiDAR data, combining rich visual appearance with accurate geometric structure. We introduce PLS-GS, a probabilistic LiDAR scaffold for Gaussian Splatting that exploits this complementary sensing setting to construct a compact 3DGS representation aligned to the measured surface. PLS-GS fits a Hierarchical Gaussian Mixture Model jointly over position and color, yielding a continuous and adaptive scaffold that eliminates the need for discrete scene partitioning such as voxel grids or octrees. The fitted mixture then guides the renderable 3DGS Gaussians: each rendering Gaussian is associated with a GMM component and constrained in the local surface frame induced by that component's covariance. A Mahalanobis support loss bounds the deviation of the rendering Gaussians from the corresponding LiDAR-derived surface. Across five large outdoor scenes, PLS-GS places primitives markedly closer to the LiDAR measurements than both image- and LiDAR-initialized baselines, while employing substantially fewer primitives, yielding a considerably more compact model and faster rendering. At matched capacity, PLS-GS places primitives closer to the LiDAR measurements, with comparable rendering quality. Code is available at https://github.com/k-harikumar/PLS-GS.

cs.CV · cs.GR · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.04483

CogniLife: Continuous Cognitive Distillation for Deployed Multi-Expert Agent Marketplaces

Xuwei Tan, Jingchun Huang, Ming Cai, Shuangquan Lyu, Tiange Wang, Yinzhi Wang, Ronghua Shi, Jingwei Song 等 15 位作者

原文摘要

Multi-expert agent marketplaces -- where creators publish domain-specific AI experts for end users to consult -- are an emerging deployment pattern for language technology. A published expert can receive many requests yet little evidence about its creator, who remains active elsewhere: the cold-expert problem. We frame continuous cognitive distillation as converting attributed creator observations into revisable decision rules, communication patterns, and domain procedures. CogniLife proposes a cross-expert attribution design for this problem, with Creator Profiles for transferable creator patterns and Expert Journals for listing-specific knowledge; this design is not yet implemented or evaluated. A deployed platform partially realizes the surrounding lifecycle through creator-authorized authoring, capability-owned proposal filtering, and reversible consolidation of eligible owner-scoped memory. On ProfyBench-v1, an 85-task offline benchmark built from replay seeds and controlled internal tests, the Profy Full configuration as a whole achieves a mean overall score of 4.65/5 from the primary LLM judge, compared with 4.01 for Raw LLM and 4.28 for Expert Prompt, with the largest gains in completeness, specificity, and missing-information tasks. These configuration-level scores do not test cold-expert mitigation, cross-expert transfer, or longitudinal distillation.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10542

When Experience Becomes a Backdoor: Poisoning Self-Evolving LLM Agents via Failure-Aware Task Optimization

Minhua Lin, Zongyu Wu, Hanqing Lu, Xianfeng Tang, Xiang Zhang, Qi He, Suhang Wang

原文摘要

Large language model (LLM) agents increasingly improve themselves through self-evolution, distilling execution experience into persistent, reusable skills. However, this persistence creates a new attack surface: malicious rules implanted into skills can compromise subsequent agent behavior. Existing skill backdoor attacks typically assume direct manipulation of the victim's skills, which is impractical when skills are constructed internally by a trusted evolver. In this paper, we study a novel problem of task-level backdoor attacks, where the attacker can only inject poisoned evolution tasks, inducing the agent to implant backdoors into its own skills. Since the attacker controls neither skill induction nor downstream execution, a natural approach is to iteratively optimize poisoned tasks based on attack success. However, our preliminary analysis reveals two key challenges: (i) failures at different stages of skill evolution and downstream execution can produce indistinguishable outcomes, providing little guidance for targeted poisoned-task updates; and (ii) unsuccessful updates may undermine earlier progress, while simply discarding them may lead to repeated ineffective updates. To address these challenges, we propose BadEvolve, a failure-aware task-poisoning framework against self-evolving LLM agents. BadEvolve diagnoses attack failures across four stages, i.e., Induction, Activation, Following, and Effect, and directs updates toward the failing stage. It further employs rollback to preserve earlier progress and failure memory to avoid repeating ineffective updates. Extensive experiments across diverse self-evolution frameworks, benchmarks, and backbone LLMs demonstrate the effectiveness of BadEvolve. Our code is available at https://github.com/ventr1c/BadEvolve.

cs.CR · cs.AI

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新论文 · v1arXiv:2611.10530

Mismeasuring the Life of a Line of Code: Lessons Learned the Hard Way

Pavel Gurov

原文摘要

Studies of how long lines of code live, including lines written by AI agents, rest on choices that look technical: when a line counts as gone, what to do with lines still alive, whether to pool projects, and how to evaluate a model. We studied 32.5 million line births in 120 TypeScript repositories and got several of these choices wrong at first. For ten of them, we compare the result as we first computed it, or as commonly done, with the result after correction. All ten change conclusions, or would have. Six were our own mistakes. Counting moved and rewritten lines as dead shortens a line's median life from 3.0 years to 1.3. Counting any change as a death hides an outcome that can be predicted: within-project concordance falls from 0.71 to 0.56. Regressing observed lifetimes discards three quarters of the lines. Keeping rewritten lines at risk makes protective effects grow with age. Lines are born and removed together, so treating them as independent misleads twice: nine of fifteen effects look significant only under that assumption, and a split by file makes a barely predictable outcome look good (within-project concordance 0.71 instead of 0.61). After the corrections, removal depends on a line's age and project, but our models could barely rank it within a project. Local revision they could rank (0.71). Obvious cases aside, the top 1% of a project's new lines are revised at 2.3 times the base rate. Each pitfall leaves a symptom; we give a checklist.

cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10488

Safer for Whom? A Systematic Exploration of AI Safety in Eating Disorder Interactions

Jinyuan Wang, Minh Duc Chu, Frances Ross, Ellen Fitzsimmons-Craft, Kristina Lerman

原文摘要

AI assistants advise users about diet, exercise, and weight loss---areas in which unsafe guidance can worsen body dissatisfaction, dietary restriction, compulsive exercise, and other behaviors associated with eating disorders (EDs). Existing evaluations emphasize diagnostic accuracy, overtly harmful prompts, or synthetic scenarios, providing limited insight into whether models recognize implicit vulnerability in natural interactions or protect users consistently. We evaluate responses from three open-weight large language models to 100 questions sourced from an online pro-anorexia community. We grade responses using clinically informed criteria that extend a seven-dimensional taxonomy of ED-related harms. We also construct matched conditions in which questions are accompanied by information about the user varying race, gender, body mass index (BMI), and explicit ED vulnerability. Safety varies substantially across models and harm dimensions. High-risk responses are approximately twice as frequent without bios, suggesting that models do not consistently recognize vulnerability from language alone. Explicit signals of ED vulnerability improve safety by 10--14 points across models. Responses also differ significantly by race, gender, and BMI, with body size producing the largest disparities. These patterns are consistent with reliance on the stereotype that EDs primarily affect thin, White women, potentially leaving users outside this profile with less protective responses.

cs.HC · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10377

Vision Language Models for Primary Structure Threat Identification in Wildland Urban Interface Fires

Zhangding Liu, Neda Mohammadi, John E. Taylor

原文摘要

Wildland urban interface (WUI) fires increasingly threaten communities and infrastructure, motivating the need for rapid visual assessment. Recent research has focused primarily on detecting fire and assessing post-event damage. However, structure-level fire threat identification during active WUI fire events remains underexplored. This study evaluates Qwen3-VL-8B-Instruct for this active-fire decision using two separately scored tracks: 1,824 synthetic images from 491 designed WUI scenarios and 45 frames from a controlled-burn experiment conducted in collaboration with Columbus Fire & EMS Department. Under zero-shot prompting, the model returned valid target fields for 1,808 synthetic images and 39 controlled-burn frames. Accuracy among valid outputs was 94.0% for the synthetic track and 69.2% for the controlled-burn track, compared with a 16.0% random-selection baseline for the latter. On the synthetic track, the model generally identified the intended target across varied designed WUI scenarios when the relevant fire-spread cues were visually distinct. Controlled-burn performance was well above random selection, but errors showed difficulty interpreting spatial relations in complex scenes. The dual-track protocol provides scalable stress testing in designed scenarios and a controlled-burn reference for assessing model behavior under physical fire conditions. Future work can use the synthetic data for VLM post-training and then evaluate the adapted models on larger and more diverse real WUI fire datasets.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10349

SituRecBench: Evaluating Frontier Models as Situated Recommendation Assistants in 3D Environments

Jingtong Yang, Dongding Lin, Jian Wang, Wenjie Li

原文摘要

Recommender systems are transitioning from static retrieval engines into interactive assistants grounded in physical environments. However, current studies largely neglect the combined complexities of spatial navigation, visual grounding, and multi-turn social interaction. To bridge this gap, we introduce SituRecBench, an interactive benchmark for evaluating frontier vision-language models (VLMs) as situated recommendation assistants within high-fidelity 3D environments. We reformulate recommendation as a situated assistance process in which an assistant interacts with a user while sharing the user's egocentric view. Our benchmark simulates real-world shopping environments (e.g., supermarkets and home furnishings stores), requiring assistants to elicit preferences, ground recommendations in the environment, and guide the user to relevant locations through dialogue. The benchmark evaluates dialogue quality, recommendation utility, and navigation effectiveness through a unified interactive protocol. Extensive experiments reveal persistent challenges in spatial grounding and preference elicitation, highlighting limitations in their ability to integrate global contextual information with local observations. SituRecBench provides a rigorous foundation for advancing the development of situated assistants in the real world.

cs.AI · cs.CL · cs.CV · cs.IR · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09734

GuardrailAgent: An Agentic Framework for Jailbreak Defense

Tianze Chang, Junjie Chu, Yixin Wu, Michael Backes, Yang Zhang

原文摘要

Despite undergoing safety training to align with human values, large language models (LLMs) remain vulnerable to jailbreak attacks that circumvent built-in safeguards and elicit malicious behaviors given by the adversary. Existing defenses are mostly static and fail to adapt to new or unknown attack strategies, providing limited resilience. To address these, we propose a multi-agent defense framework, GuardrailAgent, that offers adaptive, end-to-end protection. The system autonomously gathers and summarizes attack characteristics into a continuously updated knowledge base that supports both input- and output-side detection. A feedback module integrates misclassified cases for iterative improvement at minimal cost. Tested on over ten representative jailbreaks, the framework achieved a 93.1% input-side detection rate, 93.9% overall detection, and a 95.8% F1 score, while rapidly adapting to unseen attacks. These results demonstrate GuardrailAgent's outstanding capability to maintain LLM safety in the face of evolving jailbreak tactics.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.41177

TraceSpec: Understanding and Improving Speculative Decoding for LLM Agents

Jihao Zhao, Shuaishuai Zu, Pengfei Li, Biao Qin, Guanghuan Fang, Ben Feng, Baochuan Yang

原文摘要

LLM agents execute tasks through repeated model calls over evolving histories of reasoning, actions, and tool feedback. Aggregate speculative-decoding metrics merge these distinct decisions, providing limited guidance on where draft supervision is needed. We present TraceSpec, a framework connecting trajectory evaluation with adaptive draft training. It combines dynamic execution with fixed-prefix replay and introduces TraceSpec-Traj, a corpus of 10,000 training states and 2,500 test states spanning browsing and software engineering. Analysis of five speculative baselines reveals that aggregate acceptance can rise with context length while declining within matched late browsing actions. Workload and phase further distinguish these trends, motivating supervision guided by trajectory conditions. TraceSpec trains on intermediate assistant turns and allocates a fixed prediction budget using state-conditioned estimates of prefix-aware acceptance gaps. Across three target sizes and two temperatures, it improves replay throughput by 12.5% to 22.6% over its DSpark base. Qwen3-4B ablations show that adaptive allocation increases accepted length by 3.3% to 10.0% over fixed full-turn supervision, with additional comparisons substantiating the roles of state conditioning and consecutive-token agreement. The resulting evaluation resources, trajectory findings, and training strategy connect the analysis of agent execution to practical draft improvement.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.24371

SliceWorld: A Predictive and Controllable World-State Model for CT Report Generation

Yuanhe Tian, Yan Song

原文摘要

CT report generation (CTRG) requires models to summarize three-dimensional anatomical context and pathological findings from hundreds of axial slices. Existing methods typically learn a direct image-to-text mapping, providing limited mechanisms for modeling how CT evidence evolves across slices or how reports respond to controlled changes in latent lesion-related factors. We propose SliceWorld, a CT-specific world-state framework that treats an axial CT scan as an ordered sequence along the z-axis. SliceWorld encodes prefix CT evidence into factor-aware latent states containing anatomy, lesion, and uncertainty components, and projects these states into world tokens used for multi-step future-slice feature prediction, lesion-factor intervention, and LLM-based report generation. The model is first pretrained on CT slice sequences with predictive, factor-aware, and counterfactual objectives, and is then fine-tuned on paired CT-report data. Experiments on M3D-Cap and CT-RATE show that SliceWorld improves natural language generation metrics and clinically oriented automatic evaluation. Further analyses demonstrate multi-horizon future-slice prediction, measurable factor alignment, reduced-slice robustness, and selective lesion-sensitive report modulation. Code is available at https://github.com/synlp/SliceWorld.

cs.CV · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08929

Grid-Oriented Probabilistic Load Forecasting for AI Data Centers

Qingyi Yao, Ding Lin, Tianqiao Zhao, Meng Yue, Jianhui Wang

原文摘要

Probabilistic load forecasts for AI data centers need to represent both uncertainty under changing workloads and temporal dependence to support grid operations. This paper develops an open, grid-oriented benchmark using measured GPU power from a 448-GPU AI cluster. Forecasts are evaluated through four stages: probabilistic prediction, online uncertainty calibration, temporal scenario generation, and horizon-wide reserve sizing. Following a change in cluster utilization, a quantile LightGBM forecast achieves only 63.4% coverage for a nominal 90% interval, while online regime-conditioned calibration increases coverage to 87.3%. Even with well-calibrated marginal forecasts, independent sampling produces unrealistic ramp distributions and excessive reserve procurement. Reconstructing temporal dependence from historical forecast errors reduces the discrepancy in ramp statistics and lowers realized reserve cost by 7.4-10.6% across three forecasting tasks. These findings show that marginal forecast quality alone is insufficient for operational decisions and motivate joint evaluation of calibration, temporal structure, and decision outcomes.

eess.SY · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08868

Lodos-v2: An Efficient Primal Formulation of the Exact Frictional Contact Problem

Onur Beker, Andreas René Geist, Georg Martius

原文摘要

Lodos-v2 is a GPU-based simulation framework for massively parallel rigid-body dynamics with contact. It is specifically designed to achieve high throughput in scenes involving collisions among large numbers of detailed meshes. Its predecessor, Lodos-v1, had two main limitations. First, it solved the standard convex relaxation of cone-complementarity-based contact dynamics, thereby inheriting well-known physical artifacts, including lift-off and artificial compliance in the normal direction, as well as creep in the tangential direction. Second, its contact-group reparameterization scheme, which rescales spring-damper forces using a single factor per collision pair, could not be integrated with other constraints required by a full-fledged simulator, such as joint limits and welds. Lodos-v2 addresses both limitations through two main contributions. First, it introduces an efficient algorithm for solving the exact frictional contact problem by sequentially solving a cascade of convex relaxations in primal form within an outer proximal minimization loop. Second, it extends the contact-group reparameterization scheme of Lodos-v1 to the primal formulation of cone-complementarity-based contact dynamics, enabling its integration with the other constraints of a full-fledged simulator. Experiments demonstrate that these contributions result in throughput improvements over established rigid-body simulation frameworks. The source code is available at https://github.com/martius-lab/lodos.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02902

TailorRig：面向服装感知绑定的可控结构化辅助骨架生成

TailorRig: Controllable and Structured Auxiliary Skeleton Generation for Garment-Aware Rigging

Yajing Chen, Xin Chen, Saidan Lu, Yeqi Yuan, Weikai Chen, Changyu Hu, Cheng Zeng, Xin Wang

AI 速读 基于摘要

TailorRig 将服装感知的人形绑定重构为结构化、可控的完整骨架生成问题，通过用户指定身体骨架模板与语义标签驱动自回归生成器合成辅助骨链，并用层级链到骨权重分配减少权重渗漏。

cs.CV

arXiv ↗PDF ↗
查看原文摘要

Humanoid rigging for modern characters with complex garments remains challenging due to the need for dense auxiliary skeletons and precise skinning control. Existing automatic methods primarily recover rigs from geometry, providing limited artist control over auxiliary articulation and often lacking explicit semantic or kinematic-chain structure for dense garment bones, leading to instability and weight bleeding. We present TailorRig, a framework that reformulates garment-aware humanoid rigging as a structured, controllable full-rig generation problem. This formulation shifts the goal from automatic rig recovery to artist-driven rig authoring, where both body-template compatibility and auxiliary articulation are explicit design choices. TailorRig exposes two explicit axes of artist control within a unified framework: a user-specified body skeleton template that preserves body-rig compatibility, and artist-provided semantic labels that drive an autoregressive generator to synthesize requested category-specific auxiliary bone chains. To robustly assign skinning weights under dense skeletal configurations, we further introduce a hierarchical chain-to-bone formulation: vertices are first associated to a kinematic chain, and bone weights are then refined within that chain, reducing weight bleeding between spatially adjacent but semantically unrelated structures. We evaluate TailorRig on a diverse set of characters and introduce Human-Mix, a large-scale dataset of over 38,000 humanoid models curated for garment-aware rigging. Experiments show that TailorRig improves skeleton accuracy, deformation stability, controllability, and practical usability over existing methods. Project page: https://xxxx0601.github.io/trig/ ; code: https://github.com/Tencent/TailorRig ; dataset: https://huggingface.co/datasets/tencent/TailorRig

版本更新 · v3arXiv:2610.20424

BizAgentBench: Unveiling Hidden Challenges in Deploying Long-Horizon Agents for Real-World Business

Enhan Zhao, Yongqi Tong, Xuejun Yu, Hang Wang, Pan Wang, Yichao Wang, Di He, Jianshe Li 等 10 位作者

原文摘要

Existing long-horizon agent benchmarks often emphasize final task success while providing limited assessment of intermediate execution validity. Yet real-world business operations require agents to navigate extensive, potentially outdated or conflicting policies while satisfying critical constraints throughout execution. These requirements are particularly consequential in high-stakes domains such as payments, where intermediate violations can incur financial losses or regulatory consequences regardless of the final outcome. To bridge this evaluation gap, we introduce BizAgentBench, a multi-turn benchmark grounded in real-world procedures from a leading payments company. Its executable environment integrates 789 knowledge documents and 601 discoverable business tools, with consistent policies, states, and tool behavior. The benchmark comprises 50 tasks involving resource constraints, temporal dependencies, prior commitments, and conflicting or outdated guidance. Each task is validated through reference execution, while deterministic checks assess both goal attainment and execution validity without prescribing a single solution path. Experiments reveal substantial limitations even among frontier models: Claude Opus 5 and GPT-6 Sol achieve strict success rates of only 64.0% and 61.3%, respectively. For Qwen3.8-27B, goal attainment reaches 42.0%, but strict success drops to 32.7%, demonstrating how outcome-based evaluation can mask invalid execution. Beyond success rates, DeepSeek-V4.1-Flash averages 191.3 tool calls per run, approximately 70% more than reference executions, revealing substantial execution overhead. Overall, BizAgentBench establishes a testbed for evaluating and advancing long-horizon agents beyond task completion, toward reliable and constraint-compliant execution in complex business environments.

cs.CE

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