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新论文 · v1arXiv:2611.06627

Interactive World Simulation with Cosmos for Physical AI

NVIDIA, :, Anis Ahmad, Jon Allen, Malik Aqeel Anwar, Jesse Archer, Maciej Bala, Piotr Barejko 等 90 位作者

原文摘要

We present a shared framework that adapts Cosmos world foundation models for real-time interactive world simulation in Physical AI. By streaming visual observations in response to actions and scene controls, the framework enables users and embodied agents to interact with evolving virtual environments. It supports both predicting the visual consequences of actions and generating observations from physical state maintained by an external simulator. We demonstrate this approach across camera navigation and robot manipulation, controllable video transfer, and multi-view autonomous vehicle (AV) simulation. These examples provide a shared foundation for developing interactive virtual environments for Physical AI training and evaluation. Our code and model checkpoints are available at https://github.com/nvidia/cosmos and https://huggingface.co/collections/nvidia/cosmos3-examples. The project website is available at https://research.nvidia.com/labs/cosmos-lab/cosmos-simulation.

cs.AI · cs.CV · cs.LG · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06626

Deterministic Connectivity Sketches Under Edge Deletions

Sanjeev Khanna, Aaron Putterman, Madhu Sudan

原文摘要

How much information about a graph is needed to determine its connectivity after arbitrary valid edge deletions? This question is at the heart of designing deterministic dynamic streaming algorithms for graph connectivity. Towards answering this question, we study the deterministic one-way communication problem in which Alice holds a graph $G_A = (V, E_A)$ on $n$ vertices, Bob holds a graph $G_B = (V, E_B)$ with $E_B \subseteq E_A$, and Alice sends a message $M \in \{0,1\}^m$ that allows Bob to decide whether $G_A - G_B$ is connected. Randomized sketches solve this problem using $\tilde O(n)$ bits, whereas deterministic recovery of even one surviving edge requires $Ω(n^2)$ bits. We prove an upper bound of $m = \widetilde{O}(n^{7/4})$ bits and a lower bound of $m = \widetildeΩ(n^{3/2})$ bits for deciding connectivity. Thus, connectivity admits a subquadratic deterministic representation despite the quadratic cost of edge recovery, but remains polynomially harder than its randomized counterpart. The upper bound immediately yields an $\widetilde{O}(n^{7/4})$-bit deterministic $n^2$-fault tolerant connectivity oracle, which reports the connected components of a graph $G$ after any valid set of edge deletions. We also obtain an $\tilde Ω(n f^{1/4})$-bit lower bound for deterministic $f$-fault tolerant connectivity oracles for every $f \in [1,n^2]$. Finally, we show that the separation between connectivity and edge recovery persists in deterministic single-pass dynamic streams with bounded update multiplicity. For every fixed $ε\in (0,1/3)$, our algorithm reports the connected components of the final simple graph using $\widetilde{O}(n^{15/8 + 3ε/8})$ bits (subquadratic space), provided each edge undergoes at most $n^ε$ insertion or deletion updates in total.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06625

Dynamic Shape Control of Soft Trunk Robots

Hehui Zheng, Marc Stampanoni, Roshan S. Kaundinya, Ronan Hinchet, George Haller, Robert K. Katzschmann

原文摘要

Controlling the shape profile of soft trunk robots extends their reach into complex regions of the workspace and enhances their use in practical applications. Recent advances in soft robot design have enabled soft trunk robots to achieve greater flexibility in conforming to complex shapes. Specifically, trunk robots whose geometries are designed as helicoids have been shown to enable extreme shape-changing capabilities. Their compliance makes them highly nonlinear, which in turn makes shape control difficult. On similar tendon-driven trunk robots, model-based controllers based on the theory of adiabatic spectral submanifolds (aSSMs) have achieved high accuracy in closed-loop end-effector tracking. We extend this approach by learning an aSSM model of the full body of a trimmed-helicoid soft trunk robot from data and use this in a model predictive control (MPC) scheme to demonstrate a range of shape control tasks on hardware. On closed-loop dynamic shape regulation, our method outperforms the available linear baselines by a factor of 10. In human-in-the-loop interactive demos, our method completes section-by-section shaping and balances a tennis ball for over 25 seconds, while the linear baselines fall short. A model-free reinforcement learning policy trained for 12 hours on hardware fails on the same balancing task. These results establish aSSM-based MPC as a practical route to full-body shape control on real soft hardware.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06624

Learning to Track Cells from Sparse Annotations in 3D Time-Lapse Microscopy

Yusuke Uchida

原文摘要

Reconstructing cell lineages from 3D time-lapse microscopy requires distinguishing cell motion from division and inferring temporally consistent connections. When only a subset of cells is annotated, however, training procedures that treat unannotated regions as background or incorrect correspondences can be problematic. We propose a cell tracking pipeline that integrates detection from sparse annotations, calibration of candidate connections using image features, and joint selection of lineage graph events. For detection, we combine the outputs of 2.5D and 3D models trained to suppress erroneous background supervision at unannotated cells. We then calibrate ordinary link and division probabilities using motion features from dense flow, correspondence features from a learned cell-matching model, and features from models that identify cells immediately before division, dividing parents, and daughters immediately after division. Ordinary links, divisions represented as daughter pairs, and node inclusion are selected jointly under a surrogate objective derived from the evaluation metric, followed by gap filling and temporal coordinate refinement. The pipeline achieves an overall score of 0.9762 in 5-fold cross-validation on 199 videos from 2 zebrafish embryos. With candidates and calibrated values held fixed, joint selection outperforms a sequential method that allows replacement of existing divisions and reoptimization of ordinary links by 0.0105. The improvement arises primarily from division recovery.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06619

Projected and Reflected Sliced Wasserstein Kernels for Persistence Diagrams

Julián Uribe Castañeda, Mircea Petrache

原文摘要

Persistence diagrams do not naturally live in a Hilbert space. Carrière, Cuturi, and Oudot embedded them in kernel methods through the Sliced Wasserstein kernel, built from the Sliced Wasserstein distance $\mathrm{SW}_1$, which is conditionally negative definite (CND). We study the projected ($\mathrm{SW}_p$) and reflected ($\mathrm{RW}_p$) Sliced Wasserstein distances for every order $p\ge1$, and compare them with the $p$-Wasserstein distance $d_p$ and with a reflected variant $d_p^r$, in which projection onto the diagonal is replaced by reflection across it. We show that $2^{1/p}d_p\le d_p^r\le 2d_p$ with sharp constants, so that $d_1^r=2d_1$ and $d_p^r$ inherits the stability of $d_p$, although it is a metric only for $p=1$. The distances $\mathrm{SW}_1$ and $\mathrm{RW}_1$ are CND metrics, and $\mathrm{RW}_1$ gives rise to a new positive definite kernel, stable with respect to $d_1$. For $p>1$, neither $\mathrm{SW}_p$ nor $\mathrm{RW}_p$ satisfies the triangle inequality, and rigorous interval-arithmetic certificates prove that $\mathrm{SW}_2$ and $\mathrm{RW}_2$ are not CND; numerical experiments suggest that the CND property fails for every $p$ above some $p^*\in(1,1.2]$. On diagrams with at most $N$ points, $\mathrm{SW}_p$ and $\mathrm{RW}_p$ are metrically equivalent to $d_p$ and $d_p^r$, with sharp upper constants. On an orbit recognition benchmark, the kernels built from $\mathrm{SW}_1$ and $\mathrm{RW}_1$ reach about 85% accuracy, outperform the persistence scale space kernel, perform on par with the persistence weighted Gaussian kernel, and are about one percentage point below the kernel built from $\mathrm{SW}_2$, which is not positive definite.

cs.CG · math.AT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06615

Dimension-Free Private Mean Estimation is Impossible Without the Covariance

Renaud Gaucher, Ephraim Linder, Adam Smith, Jonathan Ullman, Lydia Zakynthinou

原文摘要

We study the limits of differentially private Gaussian mean estimation when the covariance matrix is unknown. Prior work shows that when the covariance $Σ$ is known, the optimal sample complexity can depend only on its spectrum, rather than on the ambient dimension $d$. We establish new lower bounds for Gaussian mean estimation with Euclidean error which show that private estimators cannot achieve similar guarantees when the covariance is unknown. First, when the sample size $n$ is smaller than the effective dimension $\mathrm{trace}(Σ)/\|Σ\|_{\text{op}}$, the optimal error when the covariance is unknown can be larger than the optimal error when it is known by a factor of $\sqrt{d/n}$. Second, when the sample size is larger than the effective dimension, even for diagonal covariance matrices with effective dimension bounded by $\log d$, the optimal error when the covariance is unknown can be larger than the optimal error when it is known by a factor of $\sqrt{d}/n^{3/2}$. Together, these results show that in the unknown covariance setting, exploiting anisotropic structure under differential privacy can require a dimension-dependent error or sample complexity.

cs.LG · cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2601.22183

COL-Trees: Efficient Hierarchical Object Search in Spatial Networks

Tenindra Abeywickrama, Muhammad Aamir Cheema, Sabine Storandt

原文摘要

Many spatial network applications, such as map-based services, rely heavily on efficient methods to search for relevant nearby points-of-interest (POIs). A $k$ Nearest Neighbor ($k$NN) query is one such example that finds the $k$ closest POIs from an agent's location. While most existing techniques for spatial networks focus on retrieving nearby POIs for a single agent, these search heuristics do not translate to many other applications. For example, Aggregate $k$ Nearest Neighbor (A$k$NN) queries require POIs that are close to multiple agents. $k$ Farthest Neighbors ($k$FN) queries require POIs that are the antithesis of nearest. Such problems naturally benefit from a hierarchical approach, but existing methods rely on Euclidean-based heuristics, which have diminished effectiveness in spatial networks such as road network graphs. We propose a novel data structure, COL-Tree (Compacted Object-Landmark Tree), to address this gap by enabling efficient hierarchical graph traversal using a more accurate landmark-based heuristic. We then present query algorithms that utilize COL-Trees to efficiently answer A$k$NN, $k$FN, and other queries. Our experiments evaluate performance on real-world and synthetic datasets including multiple types of spatial networks, such as road networks of varying sizes and edge weights, terrain networks, and 2D physics meshes. The results demonstrate that our techniques significantly outperform existing approaches, achieving up to three orders of magnitude improvement. Moreover, this comes at a small pre-processing overhead in both theory and practice.

cs.DB · cs.AI · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06612

From Reconstruction to Imputation: Test-Time Optimization for Missing Time-Series Channels

Björn Ludwig, Alexander Windmann, René Heesch, Oliver Niggemann

原文摘要

Complete sensor-channel failures create a difficult form of time-series missingness: local interpolation is unavailable, and deploying a separately trained imputer for every failure pattern may be impractical. We ask whether a sequence reconstructor trained without artificial mask augmentation and then frozen can be converted into an imputer at inference time. Our method treats the missing entries of each test window as optimization variables, clamps all observations, and backpropagates through the frozen model to minimize reconstruction error only on observed coordinates. The resulting observed-self-consistency objective requires neither artificial mask augmentation nor full-channel dropout during reconstructor training. We characterize when this refinement is informative through the cross-Jacobian from missing inputs to observed residuals, and use an optional temporal-smoothness prior when that coupling is weak. Across five real-world datasets with exactly one channel synthetically masked per test window and four reconstruction architectures, no-prior test-time refinement reduces MAE relative to one-pass reconstruction in 19 of 20 dataset-backbone combinations. Compared with mask-aware imputers trained under either standard structured masks or matched full-channel dropout, the best refined reconstructor achieves the lowest MAE on two datasets and trails the strongest baseline on three. These gains require substantially more inference time and are sensitive to cross-channel coupling. Our results establish test-time self-consistency as a conditional inference-time adapter for frozen reconstruction models that complements mask-aware imputation without retraining the reconstructor.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2307.04754

Dynamic Strategy Selection

Francesco Cordoni, Alessio Sancetta, Yuqian Zhao

原文摘要

We consider the problem of dynamically selecting among competing strategies using observable predictors. To mitigate estimation noise and excessive switching, we regularise the estimated policy with a switching cost, which may exceed actual transaction costs when these are too small to provide sufficient regularisation. Since switching costs make current choices affect future decisions, we formulate the problem using dynamic programming. The optimal policy is estimated using a reinforcement learning algorithm based on iterated regressions, making the procedure simple to implement. We apply the method to monthly rotation among momentum, value, profitability, and defensive U.S. equity factors over a sample of about 60 years. Using a parsimonious set of state variables, the estimated policy outperforms both the myopic dynamic policy and the best fixed factor in hindsight in out-of-sample evaluation. Under weak conditions, we show theoretically that the decision rule estimated from empirical data is asymptotically consistent for the unknown optimal decision rule. Simulation results illustrate the finite-sample relevance of these asymptotic results.

cs.LG · q-fin.PM · stat.ME · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06611

Constant-Time Dynamic Edge Coloring via Dynamic Splitting and LLL

Mohsen Ghaffari, Sijin Peng

原文摘要

We give a dynamic algorithm that maintains a near-optimal edge coloring in constant expected time per update. For every fixed $ε>0$, the algorithm maintains a $(1+ε)Δ$ edge coloring under edge insertions and deletions in $\mathrm{poly}(1/ε)$ worst-case expected update time against an oblivious adversary, provided $Δ\geΔ_ε=\widetildeΘ(1/ε^2)$. The previous constant-time algorithm of Bhattacharya, Costa, Panski, and Solomon (BCPS) required $Δ\ge(\log n/ε^4)^{Θ(\log(1/ε)/ε)}$, where $n$ is the number of vertices. We remove this dependence on $n$, answering their open question. Our approach differs substantially from that of BCPS, which relied on coloring in locally tree-like subgraphs. Instead, we draw on distributed algorithms that organize partial coloring through successive applications of the Lovász Local Lemma (LLL). We develop a dynamic LLL solver with worst-case expected update bounds and a dynamic splitting algorithm that partitions edges into low-degree subgraphs. A key technical contribution is a bottom-up method for iterative splitting, which avoids the logarithmic overhead of maintaining successive levels. It achieves constant expected update time by adaptively choosing which levels to inspect and repair, rather than inspecting every level after each update. We believe these dynamic splitting and LLL tools can be of interest beyond edge coloring.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06610

Alternatives to Shallow Autoencoders for Collaborative Filtering

Mario Mallea, Angela Nebot, Francisco Mugica

原文摘要

Collaborative filtering (CF) is a cornerstone of recommender systems and plays a relevant role in many modern applications. CF uses user-item interaction data to discover future preferences. Although deep learning models have shown promise in CF, Embarrassingly Shallow Autoencoders for Sparse Data (EASE) has gained attention for its outstanding ranking accuracy provided by its closed-form solution. EASE relies primarily on relationships among items to fit a full-rank high-dimensional linear mapping. We hypothesize that this design limits its capacity to capture similar but not equivalent fine-grained user relationships, consequently limiting its recommendation accuracy. This paper introduces an alternative formulation based on EASE that takes advantage of user-user information. Furthermore, we propose a hybrid model that combines both user-user and item-item adjacency distributions. Our experiments reveal that the proposed models outperform EASE on well-known recommendation benchmarks, highlighting the significance of including the user alternative in shallow autoencoder studies for CF.

cs.LG · cs.IR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.26647

More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations

Mingze Wang, Jinbo Wang, Yikuan Xia, Kai Shen, Shu Zhong

原文摘要

Feedforward network (FFN) layers account for a large fraction of parameters and nonlinear expressivity in Transformer-based large language models (LLMs). Despite the evolution from ReLU and GELU to gated variants such as SwiGLU, most FFN designs still use a single fixed activation function, applying the same nonlinear transformation to all tokens. In this work, we propose Mixture of Activations (MoA), a token-adaptive FFN design that mixes a dictionary of activation functions using lightweight input-dependent gates while sharing the same linear projections. As an input-independent counterpart, we also introduce learnable activations (LA), which form linear combinations of activation functions for both ReLU-type and SwiGLU-type FFNs. Theoretically, we establish strict finite-width expressive separations among fixed-activation FFNs, LA, and MoA: LA strictly contains fixed-activation FFNs, while MoA strictly contains LA, with the additional expressivity arising from input-dependent nonlinear hybridization. Empirically, we evaluate MoA through extensive pre-training experiments on dense and MoE language models ranging from 0.12B to 2B parameters under different token budgets, optimizers, and learning rate schedules. MoA consistently achieves lower terminal loss and exhibits more favorable scaling behavior than well-tuned baselines, with minimal parameter and computational overhead. These results suggest that token-adaptive activation mixing is a simple and effective mechanism for improving FFN expressivity in LLMs.

cs.LG · cs.AI · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06608

A Width Hierarchy of Monotone Algebraic Branching Programs

Julian Dörfler, Alexander Rogovskyy

原文摘要

It is well known that $\mathsf{VF} = \mathsf{VBP}_{3}$ (Ben-Or-Cleve'92), i.e. the width of any constant-width algebraic branching program can be reduced to width $3$ with only a polynomial overhead to size. We prove that this is not the case for the monotone equivalents. In particular, we prove that there is an infinite hierarchy $\mathsf{mVBP}_{2} \subsetneq \mathsf{mVBP}_{3} \subsetneq \ldots$ by showing that $\mathrm{imm}_{k,n}$ requires width $w$ mABPs to have depth at least $\left(\frac{k}{w-1}\right)^{\frac{1}{2}\left\lfloor n^{\frac{1}{(w-2)}} \right\rfloor - 1}$ whenever $3 \leq w < k$. We prove this lower bound using support-based techniques. Since this lower bound is exact instead of asymptotic, it allows some width separations even for superconstant width. Moreover, it allows us to show that the expressive power of poly-sized monotone formulas of depth $2d$ is not reached by poly-sized mABPs of width $d-1$ but a width of $2d+1$ is sufficient. We contrast this to unbounded-depth monotone formulas, which are proven to be stronger than constant-width mABPs, but weaker than $\log$-width mABPs and incomparable for most widths in between. Finally, we generalize Snir's framework (Snir'80) for showing lower bounds on the number of leaves of monotone formulas to monotone formulas of multiplication depth $d^{\times}$, giving a lower bound of $n \cdot k^{d^{\times}(n^{\frac{1}{d^{\times}}} - 1)}$ for the iterated matrix multiplication polynomial and $\left(\left(e+o(1)\right) \ln^{\circ(d^{\times}-1)}(n)\right)^n$ for the permanent.

cs.CC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06606

Verifier-Guided Self-Consistency: Test-Time Reasoning with Imperfect Generators and Verifiers

Ekaterina Fadeeva, Xueyan Li, Mrinmaya Sachan, Maxim Panov

原文摘要

Large language models can solve many multi-step reasoning problems, but their reasoning remains brittle: plausible intermediate steps often lead to incorrect answers. Improving reasoning at test time requires balancing two imperfect signals: the generator, which provides diversity, and the verifier, which guides correctness. Existing approaches largely favor one side of this tradeoff. Verifier-weighted voting relies on trajectories sampled from the generator and cannot steer generation toward correct ones. By contrast, beam-style verifier-guided search can steer generation, but may over-concentrate on narrow trajectory sets and amplify verifier errors. We propose Verifier-Guided Self-Consistency (VGSC), a stochastic decoding framework that combines step-level correctness and future-outcome estimates to guide reasoning during generation. VGSC repeatedly proposes candidate next steps from the base language model and uses both signals in an acceptance/resampling procedure, while maintaining multiple trajectories for final answer-level self-consistency. We provide a probabilistic interpretation of VGSC as approximate sampling from a verifier-tilted trajectory distribution. Across multiple reasoning benchmarks, generator models, and verifier families, VGSC achieves higher average accuracy than representative baselines.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06604

A Sub-$3^n$ Upper Bound on the Number of Stable Matchings

Ken-ichi Kawarabayashi, Hikaru Wada, Masataka Yoneda

原文摘要

We study the problem of determining the maximum number of stable matchings. This classical counting problem was posed by Knuth and has been studied for five decades. We obtain a new $2.999^n$ upper bound on the number of stable matchings with $n$ men and $n$ women. This improves the best-known previous result by Palmer and Pálvölgyi. This result gets much closer to the well-known lower bound $\frac{2.28^n}{\text{poly}(n)}$. We refine their entropy-based framework by capturing broader structural relationships among stable matchings. To capture these relationships, we introduce new frameworks, including cones and supports. This establishes the $3.332^n$ bound. We also exploit relationships among local structures and use a charging argument to improve the bound to $2.999^n$.

cs.DS · cs.DM · math.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06603

Time-Division Multiplexing: The Operational Rhythm and Temporal Organization Layer of Digital Life

Haibin Tong, Luming Guo, Nana Cui, Jiang Yu

原文摘要

Continual agents must handle urgent interaction while retaining alternatives, replaying errors, and committing safe long-term changes. A previous digital-life framework distinguishes online (busy) and offline (idle) channels, but leaves open whether these are separate programs or phases of a common process and how their operations should be ordered. Motivated by recent observations of rapidly alternating perceptual representations in primate cortex, we develop a temporal organization layer with phase-tagged micro-epochs, an observable duty cycle, independent content and resource controls, and three nested clocks separating candidate exploration from durable commitment. We give a projected duty-cycle update, state its deterministic descent guarantee and the additional noise term required for a stochastic guarantee, and specify coupled busy/idle execution contracts. Five synthetic experiments (64 seeds each) examine descent and noise, objective misalignment, gain asymmetry, resource coupling, and dropped replay logs. In the gain/noise model the best tested gain ratio is 1.55; decoupled scheduling yields 26.6% versus 11.1% success on a constructed urgent/high-load subset. A frozen-backbone Qwen3-8B prototype further checks orchestration, but its proxy outcomes are not task-benchmark evidence. The results establish a testable scheduling design and identify its conditions of validity, not machine consciousness or general-purpose agent superiority.

cs.AI · q-bio.NC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06601

AtomicFaaS: Offloading Side-Effect Compensation to the Serverless Platform

Jonas Heisterberg, Sebastian Koch, Trever Schirmer, David Bermbach

原文摘要

Function-as-a-Service (FaaS) simplifies distributed application development by offloading operational concerns to the platform, but developers remain responsible for recovering from failures. Retries require idempotent functions, which means that developers have to write complex, error-prone code that undoes or skips the already completed operations of a failed function. To address this, we propose AtomicFaaS, a platform extension that provides semantic atomicity for the external side effects of a function invocation, whether the function fails or its instance crashes, without developer-written compensation code. AtomicFaaS intercepts outbound calls and records their effects in a fault-tolerant log, from which the platform compensates leaked effects outside the failed instance. Compared with a manually implemented saga, AtomicFaaS eliminates compensation code and leaves no compensatable effects after any tested failure, while adding ~1 ms per intercepted call. Under load, it lowers the sustainable throughput of CPU-bound functions, while its latency overhead becomes negligible once calls to external services dominate the invocation time.

cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.11221

Agentic Literature Extraction Augments Targeted Protein Degradation Databases

Yaochen Rao, Farzaneh Jalalypour, N. M. Anoop Krishnan, Rocío Mercado

原文摘要

Predictive models in medicinal chemistry depend on structured assay data locked in primary publications. This bottleneck is acute in targeted protein degradation (TPD), where each assay record must combine compound identity, degradation target, recruiter, assay context, and endpoint values reported across sections, tables, and supplementary files. Existing TPD databases are manually curated and often lack assay context. We present an expert-in-the-loop large language model workflow in which a cross-validated prompt-refinement module adapts extraction instructions from scarce expert annotations. With only seven annotated molecular glue publications, the workflow achieved record-level $F_1 = 0.98$ and transferred to proteolysis-targeting chimeras (PROTACs) by terminology substitution alone ($F_1 \geq 0.93$). The resulting database, AutoTPD, contained 81% and 92% more molecular glue and PROTAC records than standardized reference datasets; expert audits found 92/100 and 33/40 new records to be correct. Applying the workflow to eligible PubMed Central open-access literature added 4,483 records from 598 publications, yielding AutoTPD+, a public, versioned database of 7,622 TPD records, including degradation assay measurements, designed for monthly updates. In a controlled surrogate model comparison, PROTAC degradation activity models trained on AutoTPD+ saw a 8.9-18.2% error reduction relative to models trained on the original PROTAC-DB data.

q-bio.QM · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06598

The Behavioral Rate of Fine-Tuning: Compression as a Measure of Useful Information

Joey David, Paul Caillon, Alexandre Allauzen

原文摘要

Low-Rank Adaptation (LoRA) adapts a frozen language model through a compact learned update, making it a natural setting for asking how much information fine-tuning needs to write. Yet adapter rank, parameter count, or numerical reconstruction error do not directly tell us how many bits are required to preserve the acquired behavior. We therefore define an operational behavioral rate $R^\star$ as the smallest serialized size of a compressed adapter that, once reloaded into the frozen base model, retains a fixed fraction of its held-out fine-tuning gain. Across LoRA fine-tuning experiments, this rate increases with distinct training content at fixed optimization budgets, but is not determined by intuitive metrics like source description length, fine-tuning gain, or weight-space reconstruction error. Most importantly, the same datasets exhibit substantially different, and even reordered, rates across frozen base models, showing that fine-tuning compressibility is a property of the dataset--receiver pair rather than of the dataset alone. However, we show that correction spectra measured on the data are able to rank these rates within a receiver. Finally, we study a striking noise-reducing effect of compression: adapters trained on deliberately corrupted datasets fall far below the frozen model, yet after compression score above it (23.7% to 82.9% on GSM8K, against 77.6% for frozen Qwen2.5-7B), consistently across tested families and sizes. The damage is unevenly concentrated in the adapter's leading singular directions, which compression appears to filter. Away from its training task, the compressed corrupted adapter also generalizes better than a clean fine-tune: on harder math it retains 97% of base accuracy, versus 44% for the clean adapter.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.21167

A Rigorous, Tractable Measure of Model Complexity

Oskar Allerbo, Thomas B. Schön

原文摘要

An accurate assessment of a model's complexity is crucial for topics such as interpretation, generalization, and model selection. However, existing complexity measures either rely on heuristic assumptions or are computationally prohibitive. In this paper, we present a mathematically rigorous yet easy-to-compute measure of model complexity that is based on the similarities between feature vectors for different inputs. It is thus well-defined for any parametric model (via NTK feature generalization) and for kernel-based non-parametric models (via the induced kernel). We prove that our measure of complexity generalizes model-specific complexity measures, including polynomial degree (for polynomial regression), kernel length scale (for Matérn kernels), number of neighbors (for k-nearest neighbors), number of splits (for decision trees), and number of trees (for random forests). We also use our measure to obtain new insights into the double descent phenomenon for random Fourier features, random forests, neural networks, and gradient boosting.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06597

A trust layer for fast pedestal-stability surrogates: calibrated error bars, out-of-distribution alarms and few-shot recalibration

Amin Vakhshouri

原文摘要

Machine-learning surrogates of linear peeling-ballooning stability now run in milliseconds, but they are mostly deployed as point estimators: they do not say how wrong they may be, or when a query lies outside what they were trained on. For a fusion company designing a new device, every equilibrium of interest is outside the training data of existing surrogates, so this is the case that matters. We describe a model-agnostic trust layer for stability surrogates, made of conformal error bars, out-of-distribution alarms that route doubtful cases to the physics code, and recalibration from a few simulations of a new design, and we test it with pre-registered hypotheses. On a large public dataset of JET-like equilibria with ideal-MHD growth rates, the error bars hold their nominal coverage in distribution and fall short under a shift in plasma shape; a few dozen simulations of the new shape, or routing the cases the alarms flag, restore it. On resistive BOUT++ surrogates, the bars stay calibrated on held-out runs, the alarm flags tokamaks absent from training, and a scenario scan is screened in seconds instead of hours. Across equilibria from the SPARC library and the DIII-D coil set, a surrogate trained on our own graded BOUT++ labels beats the baseline on unseen equilibria, while a surrogate trained elsewhere ranks the same cases backwards and its alarm declines them. Building our own neural-operator surrogate, the harness exposed two input defects whose fix closed its gap to a simple network. For every surrogate tested, the trust layer keeps coverage near target, with a bar width set by that surrogate's own error. Not every pre-registered hypothesis held, and we report those results and the limits of all of them.

cs.LG · physics.plasm-ph

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版本更新 · v5arXiv:2512.04888

ZeBROD: Zero-Retraining Based Recognition and Object Detection Framework

Priyanto Hidayatullah, Nurjannah Syakrani, Yudi Widhiyasana, Muhammad Rizqi Sholahuddin, Refdinal Tubagus, Zahri Al Adzani Hidayat, Hanri Fajar Ramadhan, Dafa Alfarizki Pratama 等 9 位作者

原文摘要

Object detection constitutes the primary task within the domain of computer vision. It is utilized in numerous domains. Nonetheless, object detection continues to encounter the issue of catastrophic forgetting. The model must be retrained whenever new products are introduced, utilizing not only the new products dataset but also the entirety of the previous dataset. The outcome is obvious: increasing model training expenses and significant time consumption. In numerous sectors, particularly retail checkout, the frequent introduction of new products presents a great challenge. This study introduces Zero-Retraining Based Recognition and Object Detection (ZeBROD), a methodology designed to address the issue of catastrophic forgetting by integrating YOLO11n for object localization with DeIT and Proxy Anchor Loss for feature extraction and metric learning. For classification, we utilize cosine similarity between the embedding features of the target product and those in the Qdrant vector database. In a case study conducted in a retail store with 140 products, the experimental results demonstrate that our proposed framework achieves encouraging accuracy, whether for detecting new or existing products. Furthermore, without retraining, the training duration difference is significant. We achieve almost 3 times the training time efficiency compared to classical object detection approaches. This efficiency escalates as additional new products are added to the product database. The average inference time is 580 ms per image containing multiple products, on an edge device, validating the proposed framework's feasibility for practical use.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06595

On Competing Sellers with Substitutable Goods

Moshe Babaioff, Yuval Grofman, Brendan Lucier, Raghuvansh R. Saxena

原文摘要

We study mechanism design by an incumbent seller facing competition from an entrant seller of a substitute good. The incumbent commits to a potentially randomized mechanism before her competitor responds. A unit-demand buyer with fixed, publicly known values for the two goods observes both mechanisms and may interact with them in either order. How should a revenue-maximizing incumbent design her mechanism when her product is superior or inferior to her competitor's? We determine the incumbent's exact optimal revenue and characterize all mechanisms that obtain it. Commitment and randomization allow her to earn strictly positive revenue even against an arbitrarily superior product, despite earning nothing under posted-price (i.e., Bertrand) competition. Optimal mechanisms can require a continuum of lottery options, even though the buyer selects only one in equilibrium: unselected options shape the competitor's incentives rather than screen private buyer information. We also show that the buyer's ability to choose the interaction order can be strictly beneficial to the incumbent, even though the incumbent is always visited first at equilibrium. Finally, we show that without commitment, every pure Nash equilibrium yields Bertrand revenues even with randomized mechanisms.

cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06594

SpikePose: Lightweight Video Human Pose Estimation

Yunhao Huang, Kaushik Bhargav Sivangi, Tianyi Yu, Fani Deligianni

原文摘要

Efficient video human pose estimation must balance localisation accuracy, motion quality, and energy cost. We present SpikePose, a lightweight video pose estimator that pairs a frame-reset, single-step spiking encoder with motion-aligned heatmap memory (MAM). The encoder supplies per-frame spatial evidence, while MAM estimates joint displacement, aligns historical heatmaps, and adaptively fuses them with current predictions. By confining temporal reasoning to a compact, lightweight module outside the spiking backbone, SpikePose helps bridge the gap between spiking and classical Artificial Neural Networks (ANN) on large-scale, non-event-driven video tasks. We evaluate spatial accuracy, motion errors, and estimated arithmetic energy on MPII and a fixed NTU60 Cross-Subject subset. Against the architecture-matched ANN with MAM, SpikePose cuts estimated arithmetic energy by 91.4% at a cost of just 1.12 points in head-bone-normalised PCK at 0.5 threshold (PCK_HB(0.5)). Within SpikePose, adding MAM reduces velocity and acceleration errors by 28.13% and 31.46% while slightly improving localisation, at a cost of only 0.003M parameters and 0.0023 analytical GFLOPs per frame. Code will be released on GitHub upon acceptance.

cs.CV · cs.NE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06593

BudgetSplat: Budget-Controllable Generalizable Gaussian Splatting

Yuxi Hu, Jun Zhang, Wei Mao, Yuchen Rao, Friedrich Fraundorfer

原文摘要

Feed-forward 3D Gaussian Splatting enables efficient generalizable novel view synthesis, but existing pixel-aligned methods often generate highly redundant Gaussian primitives, causing the final representation size and rendering cost to grow rapidly with image resolution and the number of input views. We propose BudgetSplat, a budget-controllable feed-forward framework that adapts the final Gaussian representation to a user-specified preservation ratio. Rather than treating compaction as simple importance ranking, we formulate it as a joint problem of importance estimation, spatial coverage, and cross-view redundancy. A single trained model supports varying numbers of context views and multiple test-time budgets without per-budget retraining or per-scene compression. To realize this formulation, Intra-Cross Mask Supervision provides structure-, coverage-, and redundancy-aware guidance for learning Gaussian importance, while budget-conditioned feature modulation adapts Gaussian opacity and attributes to the requested preservation ratio. Coverage-Aware Budget Selection further combines patch-wise pre-selection, view-wise opacity ranking, and 3D redundancy suppression to construct compact yet spatially distributed Gaussian representations. Experiments on indoor reconstruction and cross-dataset generalization demonstrate consistent quality--size trade-offs across different input view counts and preservation ratios, substantially reducing final representation storage and rendering cost while preserving rendering quality.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.03507

LongCounsel-8: A Benchmark Suite for Longitudinal Depression Tracking from Multi-Session Counseling Dialogues

Jiayi Li, Bingsheng He, Zhaomin Wu

原文摘要

Large language models (LLMs) are increasingly used to assess depression in counseling conversations, where psychology evidence suggests that tracking change across sessions matters more than any single score. However, existing dialogue benchmarks score each session on its own, and an accurate score for every session does not guarantee that a method follows the changes between sessions. We introduce LongCounsel-8, a benchmark for tracking depression across counseling sessions. Its systematic pipeline takes client profiles from real counseling cases, depression scores from real longitudinal data, and turn length and form from real counseling dialogues, yielding 7,749 five-session trajectories from three LLMs. Every session is generated to express a preset score on the PHQ-8, a standard eight-symptom depression questionnaire. Validation shows that the labels are self-consistent and the dialogues close to real counseling. Following clinical research on treatment monitoring, we propose a trend-based evaluation protocol reflecting real-world tracking needs: changes between sessions, crossings of the screening cutoff, trajectory types, and symptom-level changes. Extensive experiments show all five existing methods tested fall short in tracking trends: none estimates changes better than a no-change baseline, at most 40% of cutoff crossings are detected, and adding earlier sessions lowers change error but causes more missed improvements.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.41868

META-Robot: Self-Cognition and Capability Scaling in Agentic Robots

Xuanzhi Feng, Zeyu Liu, Weiheng Gao, Xinchen Li, Jiankun Tang, Fangqi Zhu, Quanxin Shou, Hao Chen 等 14 位作者

原文摘要

LLM-based robotic agent systems can generate programs for robot control, yet their generalization to new tasks often depends on elaborate scaffolding. Expanding their skill repertoires alone offers limited guidance on how to assess new capabilities or identify what remains beyond their reach. We introduce META-Robot, a framework that couples self cognition with capability scaling by expanding the criteria through which an agent evaluates and develops its skills. At its core, action-level predicates make execution outcomes explicit and available for LLM reasoning. The agent uses these assessments to identify its own limitations, extend relevant evaluation criteria, and guide the structured expansion of its skills. Proprioceptive feedback further grounds this process in the robot's physical state, enriching the evidence available for self-cognition. Experiments in simulation and on real robots demonstrate stronger overall task performance, generalization to new tasks, continued expansion of skills and evaluation criteria, and effective use of non-visual proprioceptive signals.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.34799

STRIDE: Automated Evaluation of Text-to-Trajectory Alignment across Diverse Contexts

Wanchun Ni, Tao Qi, Leonel Aguilar, Jiugeng Sun, Marlene Wagner, Verena Zimmermann, Mennatallah El-Assady

原文摘要

Language-conditioned trajectory generation is here, but its evaluation has not kept pace. Existing pedestrian trajectory metrics compare trajectories with real-world human data. This does not scale to text-to-trajectory generation across diverse contexts, as collecting human trajectories for every scenario is costly and infeasible. Moreover, pedestrian behavior is heterogeneous and context-dependent, with no single metric as the correct answer, and current evaluation frameworks are not transferable to this domain. These challenges make scalable, reliable evaluation difficult. We introduce STRIDE, the first framework for evaluating context alignment between scenario descriptions and pedestrian trajectories. STRIDE addresses these challenges through three design choices. First, we derive our VRDST evaluation protocol from sociological theories to define a complete evaluation space. Second, it decomposes high-level context into scenario-adaptive behavioral questions. Third, every question is resolved against a deterministic measurement tool library that yields reproducible answers. Together, STRIDE enables verifiable, automated, and scalable evaluation across diverse contexts without requiring human trajectory data. We instantiate STRIDE in the crowd domain as STRIDE-Bench, comprising 1K scenarios, 6K behavioral questions, and 11K measurements with calibrated expected answers across 30 real-world maps. Comprehensive human validations show that STRIDE-Bench is consistent with human behavior and judgment, achieving 80% human agreement. We further evaluate several text-to-trajectory models, finding limited context-alignment capability and persistent challenges in fine-grained context conditioning. We believe that the STRIDE framework provides a first step toward principled evaluation of context-aligned pedestrian trajectory generation.

cs.AI · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06590

Thermodynamics-Informed Learning Captures Hierarchical Hydrogen-Bond Networks in Deep Eutectic Solvents

Yanpeng Ye, Chaoran Duan, Mian Xu, S. Mani Sarathy

原文摘要

Deep eutectic solvents (DESs) offer a tunable platform for lignocellulose refining, but their solvation behavior depends on multicomponent hydrogen-bond networks rather than individual constituent properties. We develop TiNN, a thermodynamics-informed neural network that hierarchically represents HBA-HBD association and solute accommodation through a learned free-energy pathway. Trained on 938 records, TiNN achieves a test-set $R^2$ of 0.917 and generalizes to new HBA-HBD combinations and sparse conditions. Ablation identifies solute-specific stabilization as the largest contributor to prediction accuracy, while the importance of hydrogen-bond compatibility depends on the dissolution mechanism. Experimental measurements of glucose, xylan, and Kraft lignin in acidic, basic, and neutral DESs show that TiNN reduces the geometric mean solubility error from more than 23-fold with COSMO-RS to approximately 1.5-fold. Without training on fractionation data, TiNN also distinguishes DES formulations with high and low lignin and hemicellulose removal from corn stover. These results support chemically grounded solvent screening for lignocellulose fractionation.

cs.LG · physics.chem-ph · quant-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06589

Competitive Equilibrium for Linear Chores is CLS-complete

Qian Liu, Shinuo Ma, Yixin Tao, Minji Yang, Weiqiang Zheng

原文摘要

The competitive equilibrium (CE) is a fundamental concept for markets, with applications to efficient and fair allocation of resources. We consider the fundamental question of computing a CE in the most basic Fisher market model where agents have linear preferences. While it is known that we can compute a CE in strongly polynomial time with linear goods, there is no known polynomial-time algorithm for CE with linear chores. Computing a CE for linear chores is known to be in CLS with no hardness results. In this paper, we prove that computing an exact CE in Fisher markets with divisible chores and linear disutilities is CLS-hard and thus CLS-complete. To the best of our knowledge, this is the first CLS-completeness result for a market equilibrium problem. On the technical side, we use the nonconvex dual Eisenberg-Gale (EG) program whose Karush-Kuhn-Tucker (KKT) points are CE [Chaudhury, Kroer, Mehta, and Nan, 2024], and the CLS-complete problem of computing a KKT point of a quadratic program [Fearnley, Goldberg, Hollender, and Savani, 2024]. We show that from any exact equilibrium of the constructed market, we can recover in polynomial time an $\varepsilon$-KKT point of the input quadratic program over the unit box.

cs.GT · cs.CC

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