跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文，不再错过。

覆盖计算机全学科，以 AI 为快捷入口。关注方向、作者和关键词，把发现变成持续的研究积累。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+0新增论文
+0其中 AI 相关
+1已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-08 00:56 · 全站最近入库：2026-11-07 17:43各信源进度 →

统计口径与数据新鲜度

统计 2026-11-07 01:24 — 2026-11-08 01:24（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-08 00:56最近发现新增版本：2026-11-07 17:43库内论文最新更新时间：2026-11-06 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

17,100 已收录论文14,661 AI 相关4,056 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 17,100 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-08 00:54（北京时间）
版本更新 · v2arXiv:2610.24973

A Bulow-Klemperer Theorem for Bundling and Improved Approximation Bounds for Simple Auctions

Yichen Huang

原文摘要

We study simple auctions for one additive bidder whose values for $m$ items are independent. Write $\mathrm{REV}$ for the optimal revenue, $\mathrm{SREV}$ and $\mathrm{BREV}$ for the optimal revenues from separate selling and grand bundling, and $\mathrm{BSPA}_2$ for the grand-bundle second-price revenue with two independent bidders. For independent regular items, we prove $\mathrm{REV} \le \mathrm{BSPA}_2 \le 2\,\mathrm{BREV}$. Thus, one additional bidder lets a reserve-free second-price auction for the grand bundle match the optimal revenue from one bidder, regardless of the number of items. This gives a Bulow-Klemperer theorem for bundling and resolves an open question of Beyhaghi, Cai, Feng, Li, and Weinberg (EC 2025) on whether constant competition suffices for bundling. The factor-$2$ bundling approximation is also tight. For general independent items, we prove $\mathrm{REV} \le \mathrm{SREV} + \frac{π^2}{6}\mathrm{BREV} \le 2.645 \max\{\mathrm{SREV}, \mathrm{BREV}\}$. This improves the previous approximation factors of $6$ from Babaioff, Immorlica, Lucier, and Weinberg (FOCS 2014, JACM 2020) and $5.2$ from Ma and Simchi-Levi (AISTATS 2021). For the lower bound, we construct an instance in which pricing three disjoint bundles earns more than $2.11\max\{\mathrm{SREV},\mathrm{BREV}\}$, improving the previous lower bound of $2$ from Rubinstein (ITCS 2016). Together, these results narrow the tight approximation ratio to the range $[2.11, 2.645]$.

cs.GT · econ.TH

查看论文
arXiv ↗PDF ↗
版本更新 · v4arXiv:2606.28079

GTI-mSEMP Framework : A Proposed Framework to Simulate Malware Propagation with Inclusion of Attacker-Defender Strategy

Shadeeb Hossain, Elias Calboreanu, Kristopher Wilson

原文摘要

The rapid proliferation of automated, multi-vector malware threats poses a significant risk to heterogeneous, resource constrained cyber-physical networks. Conventional epidemiological models often treat security defenses as static parameters, failing to capture the strategic, asymmetric maneuvers between an attacker and a defender. To address the gap, this paper proposes a Game-Theory-Integrated Modified Multi- Wireless Sensor Epidemic Malware Propagation (GTI-mSEMP) framework. This paper analyzed and compared the operational trajectories of Susceptible (S) and Recovered (R) node populations across three different operational regimes: Balanced Matchup, Exploit Surge and Hardened Defense. Numerical simulation results capture the real-time transient dynamics of the network state variables, demonstrating how the epidemic curve shifts when either the defensive or offensive scaling vectors hold an efficiency advantage. The proposed mathematical and numerical framework provides a rigorous foundation that can be deployed in highly adversarial network environments to evaluate dynamic malware propagation and predict localized node population states.

cs.CR · cs.GT · cs.NI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05667

Correct Robots Before They Make Mistakes: Proactive Human-in-the-Loop Intervention via Preference Learning

Xinkai Jiang, Jing Jiang, Hongyi Zhou, Pankhuri Vanjani, Ahmad Baki, Jakub Suliga, Ge Li, Zhuoyue Li 等 10 位作者

原文摘要

This paper presents Proactive Human-in-the-Loop (PHIL), a framework for proactive policy correction and preference-based post-training. PHIL enables operators to preview policy-predicted future motion and intervene before anticipated undesirable actions. During takeover, PHIL continues querying the policy and displaying its predicted trajectories in the physical workspace through an extended reality (XR) interface. PHIL pairs rejected policy predictions with corrective action chunks subsequently executed from the same physical state over the same horizon, and preference-based post-training encourages the policy to favor these corrections. This paper evaluates PHIL on four real-world manipulation tasks using a diffusion policy and a pretrained vision-language-action policy. With five correction episodes per condition, PHIL increases mean success rates across the evaluated tasks and demonstration budgets from 44.0% to 70.3% for the diffusion policy and from 57.8% to 76.9% for X-VLA, compared with HG-DAgger. Separately, policies trained with proactive corrections show fewer unnecessary recovery behaviors in successful episodes. Project page: https://intuitive-robots.github.io/PHIL-page/.

cs.RO

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.13044

Robust Policy Optimization via Adversarial Importance Sampling

Amine Andam, Jamal Bentahar, Mustapha Hedabou

原文摘要

Significant progress has been made in safeguarding Deep Reinforcement Learning (DRL) policies against input perturbations. Developing robust DRL involves three main stages: algorithm design, implementation, and evaluation. In this work, we identify and address a key limitation at each stage. First, we introduce Adversarial Importance Sampling (Advis), a method that estimates and optimizes worst-case returns directly from trajectories already collected during standard training. Advis satisfies three desirable criteria that prior work does not jointly achieve: it requires no additional environment interactions, no auxiliary networks, and captures long-term robustness. Second, we introduce advrl, a modular PyTorch library that provides clean, single-file implementations of existing robustness methods and adversarial attacks, enabling rapid prototyping and reproducible evaluations. Third, we revisit evaluation under learned adversaries and show that optimal adversarial hyperparameters do not transfer across agents, which can lead to an overestimation of robustness when using a limited set of attacker configurations. Accordingly, we evaluate policies against a large and diverse set of attackers, using 6 to 14 times more configurations than prior work. Finally, experiments on continuous control environments show that Advis achieves stronger worst-case performance and greater training efficiency than existing baselines.

cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.34651

Approximating the Euclidean Ball by Polytopes: Surprising Rates in High Dimensions

Yegor Gorodzha, Steven Hoehner, Gil Kur

原文摘要

We provide sharp estimates on the number of vertices needed to approximate the Euclidean unit ball $B_d\subset\mathbb R^d$ with relative symmetric-difference volume error at most $\varepsilon\in(0,1)$. We show that the minimum vertex budget $N_\varepsilon(d)$ satisfies $$\log N_\varepsilon(d)\asymp d\log\left(1+\frac1{\varepsilon\sqrt d}\right).$$ In particular, $\exp(O(\sqrt d/\varepsilon))$ vertices suffice for every $\varepsilon\in(0,1)$ and all sufficiently large $d$. We also prove that achieving a fixed error $\varepsilon\in(0,1)$ requires at least $Ω\bigl((\tfrac{1+\varepsilon}{2\sqrt\varepsilon})^{d-1}\bigr)$ facets, an exponential number in $d$. We extend our vertex approximation result to Gaussian symmetric-difference error, and give a positive answer to the question of De, Nadimpalli, and Servedio about subexponential vertex complexity for approximating Euclidean balls. We also disprove a Macbeath-type extremality principle for unrestricted vertex approximation. Whereas the ball uniformly maximizes the inscribed approximation error among all convex bodies (Macbeath, 1951), we show that approximating the hypercube $[-1,1]^d$ to relative error $\varepsilon$, with $δ:=1-\varepsilon$, requires $N\ge\fracδ{2}\exp(Ω(δ^2d/\log^2(2/δ)))$ vertices whenever $d\ge16\log(2/δ)$. Thus, when $d$ is large, the cube is harder to approximate than the ball in this sense. Finally, we determine the sharp dimensional order of the classical Laguerre-Delone triangulation number, showing that $\mathrm{ldel}_{d-1}=Θ(d^{-1/2})$ as $d\to\infty$. This improves both the upper bound of Ludwig, Schütt, and Werner and the lower bound of Böröczky by a factor of order $\sqrt d$. By classical asymptotic approximation theorems (Ludwig, 1999), this improvement applies to every convex body in $\mathbb R^d$ with $C^2$ boundary and positive Gaussian curvature.

math.MG · cs.CG · cs.LG · math.PR

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05666

Revisiting Critic Estimation in Soft Actor-Critic Algorithms

Jan Felix Kleuker, Koen Ponse, Aske Plaat, Thomas M. Moerland

原文摘要

Soft Actor-Critic (SAC) is a widely used deep reinforcement learning algorithm that combines entropy regularization with an actor-critic architecture. Standard implementations rely on one-step temporal-difference learning of Q-functions, yet alternative critic design choices remain underexplored. In this work, we revisit three such choices: learning soft advantages instead of soft Q-values, the use of multi-step return targets, and the training of multi-step critics from replay buffer data. Keeping the policy objective fixed, we compare the six critic variants these choices generate: (i) advantage and (ii) dueling-advantage critics learned from one-step transitions, and $λ$-return and soft Generalized Advantage Estimation (GAE) critics learned either (iii, iv) on-policy or (v, vi) from replayed trajectory segments. Each variant is evaluated across an entire random hyperparameter search on fourteen tasks, treating the hyperparameters as part of the algorithm. Learning soft advantages instead of soft Q-values does not improve performance, and the soft GAE critic adds nothing over the simpler $λ$-return critic. What does improve performance, most clearly on continuous control, is training a $λ$-return critic on replayed trajectory segments, even without importance weighting or trace cutting. This extends earlier evidence that uncorrected multi-step targets work well under replay to the full soft $λ$-return, entropy terms included. By design, the underlying ideas studied here extend beyond SAC, and our empirical findings aim to encourage their application to a broader class of reinforcement learning algorithms.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05665

Construct Validity of Coding Benchmarks for LLM-based Agentic Coding Tools

Alexander Berndt, Paul Ralph, Thomas Bach, Sebastian Baltes

原文摘要

Software developers increasingly adopt LLM-based agentic coding tools to assist them with development tasks. To provide developers with up-to-date information on these tools' effectiveness, prior work introduced benchmarks that enable comparisons across tools. Despite the widespread adoption of such benchmarks, however, recent work has shown that they suffer from issues threatening their validity. Aims. In this work, we aim to complement prior work by analyzing the construct validity of agentic coding benchmarks. Methodology. We analyze nine benchmarks commonly used to evaluate the effectiveness of agentic coding tools. We identify the constructs that the benchmarks claim to measure, the metrics used to operationalize them, and potential confounders that interfere with the measurement. Results. Our findings show that benchmarks commonly operationalize broad concepts such as coding capability with only a small set of metrics. The reported attributes that might confound the measurements vary between benchmarks, indicating a lack of standardization. Conclusions. We conclude with recommendations to strengthen construct validity in agentic coding benchmarks and encourage further empirical work to better understand the effectiveness of agentic coding tools.

cs.SE

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05664

Combining Relevant Expert Knowledge Using Mixtures of Feedback Fuzzy Cognitive Maps

Olaoluwa Adigun, Akash Kumar Panda, Bart Kosko

原文摘要

A neuro-symbolic system should combine only relevant answers from expert agents or other knowledge sources. Using only relevant experts for a given input query becomes ever more important for accuracy and scalability as the knowledge network grows and connects ever more knowledge sources. We extend the standard definition of relevance from the legal theory of evidence: An answer is relevant if and only if it is both probative and material for the matter at hand given the knowledge available. We apply this relevancy scheme to mixing the feedback fuzzy cognitive maps (FCMs) of real or artificial experts. The expert FCM directed causal graphs mix or combine into a common knowledge network. We compare the equilibrium limit-cycle predictions or answers of the experts to the answers of the mixture FCM and then estimate the probative status of answers by their difference or variation. A new Bayes-like theorem de-mixes the knowledge mixture into corresponding posterior-like FCMs that also allow relevancy-based comparisons. The result is a final set of relevancy mixing weights that produces a relevancy-mixed FCM knowledge network for the given query. So different queries produce different relevancy-mixed FCMs. We demonstrate this relevancy approach using generated synthetic FCMs from an actual 13-node expert FCM that models the causes and feedbacks of ischemic-stroke risk.

cs.AI · cs.IR · cs.SC

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.07163

Rate-Fidelity Control for Wide-Area Quantum Links

Connor Clayton, Cory Nunn, Quinn Carmack, Wayne McKenzie, Anne Marie Richards, Xiaodi Wu, Bobby Bhattacharjee

原文摘要

Quantum network links must distribute entanglement at high rates while satisfying application-specified fidelity demands. However, wide-area deployed fiber links suffer from polarization drift which destabilizes end-to-end fidelity and forces periodic compensation. Current deployments often use active stabilization with fixed control policies, and improvements generally stem from advances in quantum hardware. Meanwhile, software control remains relatively underexplored. Here, we formulate quantum link operation as a joint control problem over tunable rate-fidelity tradeoffs and uncontrollable link drift. From this framework, we construct a link control protocol that dynamically adapts source pump power and polarization compensation to maximize entanglement distribution rate subject to a minimum fidelity constraint. We evaluate the protocol through trace-driven simulations driven by data from a 64 km deployed optical fiber. Compared with optimized static policies, our adaptive controller improves mean entanglement distribution rate by 14% over a 24 hour trace, without requiring any offline policy optimization. Our results show that software-based physical layer control can provide a practical mechanism for improving near-term quantum link performance without requiring additional quantum hardware.

quant-ph · cs.NI

查看论文
arXiv ↗PDF ↗
版本更新 · v4arXiv:2512.10888

PubTables-v2: A new large-scale dataset for full-page and multi-page table extraction

Brandon Smock, Maxim Sokolov, Libin Liang, Valerie Faucon-Morin, Amrit Ramesh, Tayyibah Khanam, Maury Courtland

原文摘要

Table extraction (TE) is a key challenge in document understanding. Traditional approaches detect tables first, then recognize their structure. Recently, interest has surged in developing methods, such as vision-language models (VLMs), to extract tables directly in their full page or document context. However, a lack of annotated data has made progress difficult to demonstrate. To address this, we create a new large-scale dataset, PubTables-v2. PubTables-v2 unifies TE across various levels of surrounding context and, notably, is the first benchmark for multi-page TE. Our evaluations reveal that while current frontier models strongly outperform ($+0.354\ \textrm{GriTS}_\textrm{Con}$) small models on the most complex task (full-document multi-page TE), this gap can be closed or even reversed ($-0.056\ \textrm{GriTS}_\textrm{Con}$) on narrower tasks (cropped table extraction) with targeted training. Data is available at https://huggingface.co/datasets/kensho/PubTables-v2.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05662

Grounded but Not Immune: Confirmation Bias in Agent-as-a-judge

Drake Benjamin Brown, Khushbu Pahwa, Yubin Ge, Salvatore Romeo, Akhil Agnihotri, Yi Zhang, Xiaofei Ma, Daniele Bonadiman

原文摘要

Evaluating open-ended agent tasks increasingly falls to an Agent-as-a-Judge: an evaluator handed tools and the agent's full trajectory so it can verify a result against the environment. The premise is that a judge that gathers its own evidence should be grounded by it. We show that this grounding does not immunize the judge against confirmation bias: a single false pre-screening note, attributed to an authority and carrying no evidential bearing on the correct verdict, overturns up to 40% of a tool-using judge's correct verdicts across four judges and five datasets, even though the judge collects decisive evidence. The shift is driven by specific properties of the cue. We treat this cue as a controlled diagnostic of confirmation bias rather than an adversarial attack. Our central contribution is to explain how confirmation bias changes the judge's behavior in a trajectory. We classify changes in judge behavior and attribute changes to one of three failure modes: over-trust (verify, conclude correctly, then defer), hallucination (fabricate support for the flip), and under-investigation (stop at the first corroborating signal). For the two Claude judges, the failure is predominantly over-trust (67-75% of harmful flips), while smaller judges have error modes spread across all three. Lightweight mitigations blunt but never remove the bias.

cs.AI · cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05660

AQUA - A Modular Authentication Quality Modeling Framework

Alex R. Mattukat, Michael Zerbe, Krisa Carka

原文摘要

Authentication solutions are the key design, architectural, and implementation decisions that implement authentication security controls. An example is a password-based authentication solution. While researchers often examine authentication quality, existing approaches do not distinguish which effects on quality stem from which aspect of authentication. For example, multi-factor authentication solutions tend to be less usable because they combine multiple authenticators, whereas passkeys are more secure than passwords because of properties inherent to the authenticator itself. This paper presents AQUA, a modular quality modeling framework for authentication solutions. AQUA's core idea is to decompose authentication into four interrelated concepts, enabling modular specification of authentication quality and tracing quality effects back to their source. We demonstrate AQUA with initial definitional quality models that address the levels of authenticators, authentication techniques, and authentication solutions. We further illustrate the interrelations between these concepts through concrete examples and a usability metric, showing how quality propagates across the concepts and ultimately affects the overall quality of an authentication solution. Our results provide a foundation for future work, including designing assessment and prediction models for authentication solutions and automated quality assessments.

cs.CR · cs.SE

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05659

Adaptive Domain Decomposition for Finite Basis Physics-Informed Neural Networks

Sarah Helfert, Wanxin Chen, Amanda A. Howard, Panos Stinis, Alexander Heinlein

原文摘要

In this work, we present adaptive domain decomposition finite basis physics-informed neural networks (ADD-FBPINNs). The proposed method integrates Partition of Unity Networks (POUNets), a data-driven approach for learning domain partitions, with finite Basis physics-informed neural networks (FBPINNs) to perform more efficient domain decomposition and achieve more accurate approximations. FBPINNs improve upon the accuracy of standard physics-informed neural networks (PINNs) through user-defined subdivisions of the computational domain. By adapting the domain decomposition based on the solution's features using a separable POUNet, the framework can automatically learn partitions that are stable and can be geometrically interpretable. We test this approach on the two-dimensional Helmholtz equation, two-dimensional Poisson equation with multiscale features, and viscous Burgers equation and observe improved accuracy compared with FBPINNs with fixed domain decomposition.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05658

Beyond Accuracy: Information-Theoretic Decoder Evaluation Under Latent Intent

Tasha Kim, Benjamin Ballyk, Anson Lei, Ingmar Posner, Oiwi Parker Jones

原文摘要

A central challenge in assistive brain-to-robot control is decoding a user's latent intent from complex and noisy brain signals. Prior approaches evaluate decoders as single-trial classifiers with accuracy-based metrics, but this neglects the inherently sequential nature of assistive control. Since a planner must integrate intermittent, noisy decoder outputs over time, single-step accuracy can only be a proxy when predicting downstream task performance. We formalize brain-to-robot control as a partially observable Markov decision process (POMDP), and prove that there is a sharp phase transition: regret is uniformly bounded across the horizon as long as pairs of goals are all decoder-distinguishable, but grows linearly otherwise. We show that a single channel statistic, the worst-pair Chernoff information $Γ_{\min}$ sets this boundary. Then, we derive a closed-form regret predictor $U_{\mathrm{id}}(C)$, that can be computed from any confusion matrix in $O(K^2)$ time. Across two domains spanning orthogonal information channels, we show that $U_{\mathrm{id}}$ predicts cumulative planning regret beyond what accuracy can explain. We then highlight a pathological accuracy trap, in which two decoders have identical validation accuracy 35.1% on real EEG data (BNCI2014001 subject 5) but result in a difference of $9.6\times$ in held-out regret, which $U_{\mathrm{id}}$ predicts. Same-signal control confirms $U_{\mathrm{id}}$'s advantage with decoders that span independent feature channels. Ultimately, our work provides a drop-in diagnostic to evaluate and select sensors and decoders in sequential assistive control.

cs.LG · cs.RO

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.17154

Neuromorphic Decoding and Dynamical Image Reconstruction from Populations of Spiking Retinal Ganglion Cells

Ning Zhang, Arto Nurmikko

原文摘要

Decoding visual stimuli from the spiking activity of retinal ganglion cells (RGCs) informs both our understanding of the retinal code and the design of visual prostheses. Natural vision is dynamic, yet state-of-the-art decoders mostly reconstruct single frames with artificial neural networks that process dense, continuous-valued signals rather than the retina's sparse, asynchronous spikes. We asked whether dynamic scenes can be reconstructed and tracked entirely within the spike domain, and at what computational cost. We built an end-to-end spiking neural network (SNN) whose neurons integrate the recorded RGC spikes over time. Populations of up to 1836 RGCs were recorded ex vivo from mouse retina on high-density microelectrode arrays while flashed images, moving bars, drifting gratings and randomly moving handwritten digits were presented. For moving digits, one shared spiking encoder fed two readouts, one reconstructing the image and one decoding the digit's position. Compute energy was estimated from counted synaptic operations. The SNN decoded what and where at once. It reconstructed the stimuli with a structural similarity (SSIM) of up to 0.93, refining the reconstruction as spikes accumulated, and tracked a randomly moving target with a mean error of 1.33 pixels on a $32\times32$ grid. Its fidelity nearly matched that of the best of 17 larger spiking and artificial decoders (5.9 million to 1.7 billion parameters, versus 5.4 million), and at a mean firing rate of 14.2% it needed an estimated $\sim$70-fold less compute energy than a dense artificial network of the same architecture. Dynamic visual scenes can thus be decoded from retinal population activity in the spike domain, with a compact network and at a compute cost compatible with implantable and wearable neural interfaces, supporting spike-native decoders for visual prostheses.

cs.ET · cs.LG · eess.SP

查看论文
arXiv ↗PDF ↗
版本更新 · v4arXiv:2609.14101

On the Complexity of Finding Fixed Points for Set-Valued Contractions

Emmanouil-Vasileios Vlatakis-Gkaragkounis, Pucheng Xiong

原文摘要

We study the computational complexity of finding fixed points of set-valued contractions. We first formulate a computational problem for Nadler's fixed-point theorem, PROJECTED-NADLER, and prove that it is CLS-complete by showing its equivalence to CONTINUOUS-LOCALOPT. We then establish a stronger converse to Nadler's theorem, which can be applied as a tool to analyze the convergence rate of the set-valued basic iteration procedure. Finally, we reduce the large-margin triplet stationarity problem to PROJECTED-NADLER. Combining the argument for CLS-hardness of exact triplet stationarity problem [Yan et al. 2025] with the CLS-hardness of approximate KKT points of quadratic programs [Fearnley et al. 2025], we obtain CLS-completeness of large-margin triplet stationarity at any prescribed accuracy.

cs.CC · math.GN

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05655

When Are Two SAE Latents the Same Feature?

Alexandra Suvorikova, Ilgam Latypov, Anton Trukhan, Yuriy Dorn

原文摘要

Comparing sparse autoencoders (SAEs) requires connecting feature correspondence, differences between whole dictionaries, and uncertainty from finite evaluation data. We develop a framework that connects these tasks through an energy-weighted optimal-transport plan under a specified similarity geometry. The same plan defines a dictionary-level discrepancy, decomposes it into latent contributions, and quantifies how each latent's mass is distributed across counterparts. We characterize relationships between decoder, activation, and first-order causal similarities; idealized models clarify the interpretation of splitting and absorption. For fixed trained SAEs, we derive joint asymptotic inference for transport costs and fractions, accounting for activation similarities and both energy marginals estimated from the same documents. A joint document bootstrap gives asymptotically valid simultaneous confidence bands. Experiments across four language models locate cross-seed discrepancy and separate context-dependent changes.

cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05652

Quantifying Trait-State Hierarchy in EEG Foundation Model and Signal Processing Embeddings

Anusha Madapura, Aditya Kommineni, Shrikanth Narayanan

原文摘要

Electroencephalography (EEG) measures brain activity through recording electrical potentials of the scalp. EEG recordings capture two distinct sets of characteristics in the signal: (1) \emph{Traits} -- information that remains relatively constant over the course of a recording session, such as subject identity, age, and sex; (2) \emph{States} -- signatures that encode responses to external stimuli, including visual, auditory, and motor information. There is limited understanding of how different EEG modeling features (signal processing and foundation models) encode state and trait characteristics. In this work, we quantify state- and trait-level features captured by EEG embeddings using a hierarchy metric and kNN purity on Brain-Computer Interface dataset trials, and find that a majority of EEG features form a macro-trait hierarchy (embeddings form trait-level clusters with micro-states within). Our linear probe results indicate that this hierarchy structure leads to suboptimal state distinguishability, negatively impacting BCI performance of EEG foundation models. To mitigate this, we explore normalization techniques that reduce trait-specific information in embeddings and provide a $\sim$ 10\% improvement in balanced accuracy for state classification. Additionally, we observe that when fine-tuning models for downstream state decoding, embedding spaces transform from macro-trait to macro-state hierarchies, which in turn lead to better state decoding.

cs.AI · cs.LG

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06955

A Sharp Additive Bound for the Threshold Rule in Repeated Rent-or-Buy

Vasco Schiavo

原文摘要

Motivated by context management in LLM agents, we study a repeated rent-or-buy model with schedule-independent history growth and a fixed reset price $K$, a discrete form of dynamic TCP acknowledgment: every call pays as rent the history accumulated since the last reset, and a reset clears it. The classical threshold rule is $2$-competitive, and no deterministic rule guarantees a smaller ratio. We show that, measured additively, its loss is governed by the changes in the growth: if the growth is constant on $P$ runs, the threshold rule, knowing neither the runs nor the horizon, pays at most $(2P-1)K$ more than the optimal schedule chosen with hindsight, and the coefficient $2P-1$ is sharp. If the growth is only within a factor $1+\varepsilon$ of $P$ runs, the bound holds up to a factor $1+\varepsilon$ and an extra $\varepsilon K$. The proof is a potential argument built from the relative value functions of the single-rate problems. On two runs no deterministic rule guarantees a loss below $2K$. A rule that also resets when the growth decreases guarantees $2K$, while the threshold rule can lose arbitrarily close to $3K$.

cs.DS

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05650

Attributing Agent Failures to Their History Needs an Executed Counterfactual: Toward a Reporting Standard

Egor Pakhomov, Erik Nijkamp

原文摘要

Published explanations of why long-horizon agents fail are commonly taxonomies labeled over completed trajectories: a judge reads a transcript and names the failure. We argue that a claim attributing an agent's behavior to its history should carry intervention-verified evidence, per instance or via a validated estimator: freeze an executable mid-trajectory state, alter only the history the agent sees, and continue - executed rather than inferred from the transcript, at a state whose restoration has been measured, with a replay certificate matched to the estimand. Each of the four manipulation classes we name has a precedent, and several methods already execute interventions from checkpoints, re-executed prefixes or recorded conversation states (for example DoVer, REFLECT, Min et al., Who&When Pro, Causal Agent Replay), but none reports a measured replay certificate. Two cautionary measurements, neither applying the standard, follow: a carrier taxonomy whose "concentration" could not be separated from benchmark construction (adjusted odds ratio 1.15, interval crossing 1), and hash-identical snapshots that, restored into identically seeded processes, agreed with one another in 24 of 24 states while differing from the live continuation in 9 and 20 of 24. Restoring the captured position of a random stream made all 24 agree with the live run.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05648

NitroFusion2: Bringing Adversarial Loss to the Text Stream of Multimodal Diffusion Transformers

Dar-Yen Chen, Yi-Zhe Song

原文摘要

Adversarial diffusion distillation has conventionally relied on image features for discrimination. We revisit this choice for multimodal diffusion transformers (MMDiTs), whose text stream becomes image-dependent through joint attention. We introduce Text-Stream Adversarial Distillation (TSAD), which exploits intermediate text-token states as a new adversarial representation. Because the text stream has already read the image while remaining tied to the text condition, changing that condition changes what information it extracts from the image. Building on this property, we introduce Aspect-Cued Discrimination (ACD), which conditions the discriminator on different aspects of the sample, such as typography, objects, or style, to steer which properties are emphasized during adversarial training. Building on the expert-discriminator design of NitroFusion, we integrate TSAD and ACD in NitroFusion2. NitroFusion2 brings few-step distillation from the conventional 4-8-step regime down to just 1-2 steps at comparable quality. At one step, it produces sharp rendered text and rich fine-grained details, making one-step generation practical rather than merely a stress test for distillation. At two steps, it remains competitive across the evaluated benchmarks. These results establish the MMDiT text stream as a powerful and steerable representation for adversarial diffusion distillation.

cs.CV

查看论文
arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.18902

SAGE: Stochastic Prompt Optimization via Agent-Guided Exploration

Ziyi Zhu, Luka Smyth, Saki Shinoda, Jinghong Chen

原文摘要

Context engineering has emerged as a primary lever for improving AI systems without parameter updates. Recent work showing that textual gradients do not function as real gradients motivates treating automatic prompt optimization (APO) as black-box search. We introduce SPO (Stochastic Prompt Optimization), a framework for stochastic search over prompt space, and compare three strategies of increasing sophistication: error-informed random search, a genetic algorithm with evolutionary operators, and SAGE (SPO via Agent-Guided Exploration), a multi-agent pipeline with diagnostic code execution. Across three benchmarks, no single strategy dominates; effectiveness depends on the interaction of landscape structure with error type. We further deploy SAGE on a mental-health chatbot under a continuous optimization paradigm, where it compounds eight cycles of individually-noisy A/B tests into a statistically robust gain in next-day retention. We argue that coupling qualitative diagnosis with quantitative validation is what makes agentic optimization effective for open-ended task-oriented dialogue.

cs.CL

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.01546

The General Stability of Ranking

Houming Chen, H. V. Jagadish

原文摘要

Rankings derived from weighted scoring functions are widely used in university rankings and employment candidate evaluations. Since ranking weights are often chosen by organizations or analysts, ranking stability asks whether a reported ranking persists under reasonable weight changes. Prior work defines volume-based stability as the fraction of the weight space that induces the target ranking exactly. This exact-match requirement can be too blunt: once a perturbed weight vector produces a different ranking, exact stability gives it no credit, whether the change replaces the top-ranked item or only swaps two nearly tied lower-ranked items. We propose general stability, a distance-based generalization that aggregates ranking regions according to a user-defined distance from the target ranking. This lets users specify which ranking changes matter in the application, while recovering exact stability as a special case. We extend existing methods and identify distance properties that permit faster general-stability computation with accuracy guarantees. Our sampling algorithm, $GSC_{md}$, can take exponential time in the worst case, but a property we call uniform boundedness gives a sample budget independent of database size and dimension at fixed accuracy and confidence. We develop Prefix-GSC, a sampling optimization that reduces sorting for distances satisfying a further property, tail decay with bounded influence. We also develop Conv-SC to approximate stability through convex volumes for distances satisfying a property we term quasiconvexity. Experiments on seven real-data sources and generated inputs show how distance affects robustness conclusions and when the algorithms save time. On three 500-item university datasets, Prefix-GSC achieves median query speedups of $1.22$--$1.98\times$ over $GSC_{md}$ at 2% relative error and 95% confidence, including method setup.

cs.DB

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05646

ARIDA - Active railway infrastructure dataset annotation using neural network predictions

Maximilian Dornik, Christian Sieberichs, Simon Geerkens, Alexander Braun

原文摘要

Modern railway perception requires large comprehensive image datasets, but creating them is slowed by manual labeling, especially for track polylines and other complex infrastructure geometry. This paper presents the Active Railway Infrastructure Dataset Annotation framework, a railway-specific semi-automated pipeline that converts instance predictions from a mask scoring instance segmentation network into bounding-boxes, polygons, polylines, and paired polylines. To keep automatic track geometry annotation reliable without ground truth, it applies confidence thresholds and an unique contour-agreement quality score as an acceptance gate. An active learning loop ranks remaining images by uncertainty and adapts the number of manual queries per cycle. This is achieved through a novel quantity control mechanism, in which easy images are auto-accepted and human effort targets hard cases. Experiments on disjoint, diverse imagery and time-sequential, redundant imagery quantify effort reduction and accuracy trade-offs, achieving about two thirds reduction in the disjoint regime and up to 93% in the sequential regime. The study also provides guidance for initial label budgets and stopping to avoid over-annotation.

cs.CV

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05643

Data-Efficient Scaling-Law Discovery for Recommendation Systems via Coupled Hyperparameter Scaling

John Martabano Landy, Louis C. Tiao, Yao Xuan, Luc Kien Hang, Rupinder P. Khandpur, Arvind M. Vepa, Srinidhi Raghavendran, Anirudh Agrawal 等 13 位作者

原文摘要

Large-scale ranking and recommendation models of Web platforms benefit from increased compute much as large language models do, and their scaling behavior is broadly power-law in form. Yet ranking models have heterogeneous architectures which makes their scaling law less straightforward than that of LLMs. We address how to discover the scaling law of an arbitrary ranking model efficiently. We introduce a scaling layer, which groups primitive architecture parameters into semantically coherent groups and defines how they co-vary, inducing a mapping from a low-dimensional space of hyper-hyperparameters into the full architecture space. On top of it, Axia fits scaling-informed surrogates, selects configurations by a posterior-based acquisition criterion, and returns a Pareto frontier from which per-model operating points are validated -- replacing exhaustive sweeps with a small number of informative runs. A fleet-level evaluation protocol, computed on a fixed representative model set under matched trial budgets, makes refinements to either component measurable. Our contribution is an automated system that learns model-specific complexity-quality relationships and gives uncertainty-aware guidance for allocating compute across a heterogeneous model ecosystem. Across 34 model iterations in two development cycles on Meta's ads ranking and retrieval system, the workflow yielded a mean 0.66% offline normalized-entropy improvement and 0.36% cumulative online lift.

cs.LG · cs.IR

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05642

Runway Characters: Serving a 57B Video Diffusion Transformer for Real-Time Conversation

Rohan Agarwal, Robin Andeer, Alex Armbruster, Bennett Arthur, Michail Christos Doukas, Piero Esposito, Qiming Fang, Aron Filkey 等 33 位作者

原文摘要

Conversational video characters must generate video as fast as it plays, for one user at a time: at 24 fps, a sustained 166.7 ms per four-frame chunk at batch size one, with only a few chunks of buffering. We describe the system behind Runway Characters, which serves a $\approx$57B-parameter autoregressive video diffusion transformer (four denoising passes per chunk) at 704$\times$1088 and 24 fps on four H100 GPUs with a voice agent in the loop. Each chunk takes 240 attention calls, each reading a 5,788-slot KV cache in which only 757 tokens are new, and 29% of the original step was whole-cache work repeated in every call. We move that work to write time (a ring cache, RoPE and FP8 quantization applied once per write, and a delta rotation when positions reset), fuse the tensor-parallel all-reduce into the FP8 GEMM with in-switch reduction, and give the decoder that turns latents into video frames its own GPU. Without the fused kernel, our optimizations cut the diffusion step from 240.9 to 159.3 ms (1.51$\times$, 24.5 fps sustained) with no detected degradation in lip-sync scores; the fused kernel, rolled back once in production and since re-enabled, reaches 147.4 ms (1.63$\times$, 43% of FP8 peak). In seven weeks of production the worker averaged above 23.5 fps in 99.5% of sessions that streamed for at least a second, and our pipeline takes 0.49 s of a 2.1 s median conversational turn. On the same GPUs, the two open-source real-time video systems we benchmarked, at four denoising passes, miss real time or add over a second of latency (one by estimate), at a third to half of our resolution.

cs.DC

查看论文
arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.01865

CyclicJudge: Mitigating Judge Bias Efficiently in LLM-based Evaluation

Ziyi Zhu, Olivier Tieleman, Alexey Bukhtiyarov, Jinghong Chen

原文摘要

LLM-as-judge evaluation has become standard practice for open-ended model assessment; however, judges exhibit systematic biases that cannot be averaged out by increasing the number of scenarios or generations. These biases are often similar in magnitude to the model differences that benchmarks are designed to detect, resulting in unreliable rankings when single-judge evaluations are used. We introduce a variance decomposition that partitions benchmark score variance into scenario, generation, judge, and residual components. Based on this analysis, CyclicJudge, a round-robin assignment of judges to scenarios, is demonstrated to be the optimal strategy for a fixed judge panel and judge-call budget: it cancels the average additive judge offsets while matching the cost of single-judge evaluation. Empirical results on MT-Bench and MindEval validate the effectiveness of CyclicJudge as predicted, across both general-purpose and domain-specific evaluation settings, reducing benchmark score variance and stabilizing the resulting model ranking.

cs.CL

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05638

VIPBench: A Human-Aligned Benchmark for Voice Identity Perception in the Age of Voice Cloning

Xudong Tang, Matthew Groh

原文摘要

Speaker-recognition benchmarks test how accurately a model recovers the speaker label of a recording, treating voice identity as a property of the person who produced it. However, these benchmarks do not evaluate how humans perceive voice identity. Whether a listener hears two voices as the same person has consequences for intellectual property rights, cybersecurity, and everyday life. We introduce the Voice Identity Perception benchmark (VIPBench), which contains 124,876 same-or-different judgments from 1,290 listeners on 9,800 recording pairs from 100 public figures. The pairs include real recordings, voice clones generated by a text-to-speech system, and morphs between two speakers' voices. We also define four tasks that evaluate the alignment between speaker-representation models and human identity perception. The first two tasks measure how well a model's similarity score predicts the proportion of listeners who respond "same" and the majority response. The other two measure whether a model and listeners find the same speaker pairs similar, and whether a model's relationship to listener judgments holds equally for real and synthetic recordings. We evaluate eleven publicly available speech models. Listener judgments diverge from speaker labels most often for voice clones: in 41% of responses, listeners judged a clone and a real recording of the same speaker to be different people. No model reaches the noise ceiling; the best model explains 61% of the explainable variance. Models trained with speaker labels align with listeners better than self-supervised models do, yet a model with a 12% error rate on speaker labels aligns with listeners as well as models with error rates below 2%. VIPBench enables the evaluation of speaker models against human perception on real, cloned, and morphed speech, and our results motivate training speaker models directly on listener judgments.

cs.SD · cs.AI · cs.CY · cs.MM

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05636

Route to Remember: Adaptive Memory Selection for LLM Agents

Zeyu Fang, Jun Yin, Yongsheng Mei, Xu Han, Chaitali Joshi, Zhongkai Sun, Jie Hao, Bin Guo 等 11 位作者

原文摘要

The memory of production agents is increasingly becoming a bottleneck and is built almost exclusively from text: key points are extracted from out-of-limit context and retrieved into context at query time. Therefore, every new query incurs an additional cost that grows with the accumulated history. Weight memory is a complementary alternative that internalizes knowledge into model parameters and answers with no context at all, and has recently proven effective in self-evolving agents for internalizing skills and behaviors. However, its application in episodic and semantic memory, such as user-specific facts, remains comparatively unexplored, and a gap remains between earlier attempts at weight memory and text-based systems regarding recall performance. We study how to make weight memory competitive as a long-term factual store by applying on-policy self-distillation (OPSD). It trains a context-free student from a teacher, which is the same model conditioned on the user's memory, together with a verify-then-regenerate router that lets weight and text memory each cover the queries they handle best. On the HaluMem benchmark, OPSD matches or surpasses strong text-memory baselines, including full-context prompting and retrieval systems, while reading fewer tokens per query. We further conduct an extensive study that characterizes the behavior of weight memory and analyzes how different training recipes affect its performance. As agent harnesses move toward persistent, multi-session deployments, these results position weight memory as a practical, low-cost component of agent memory and provide insights into feasible training and evaluation protocols for combining it with retrieval.

cs.AI

查看论文
arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05633

Spatio-Temporal Multi-Order Interaction for Video Recognition

Kevin D. Nguetche, Lilian Hollard, Eric Granger, Marco Pedersoli

原文摘要

Many temporal approaches for video recognition rely on explicit cross-channel aggregation projections, which have quadratic complexity in the number of channels, introducing significant computational overhead. In addition, the spatio-temporal representation used for video recognition is biased either toward local order interactions (for CNNs) or toward global order interactions (for transformers). Starting from the multi-order spatial representation of MogaNet, our method introduces two complementary contributions to tackle these problems. First, for the temporal modelling, we introduce parallel multi-order processing with depthwise-only convolutions and implicit gating-based aggregation, which is linear in the number of channels, inducing a considerable computational reduction. Second, we propose a dual interaction between spatial and temporal dimensions, which allows a better spatio-temporal representation without increasing the computational cost. By combining these two contributions, our proposed VideoMogaNet achieves performance on par with or better than state-of-the-art action recognition approaches across multiple video benchmarks (UCF101, HMDB51, Kinetics-400, Diving48), while considerably reducing the cost of the temporal branch.

cs.CV · cs.AI · cs.LG

查看论文
arXiv ↗PDF ↗