Machine Learning
Differentially Private Mixing of Public Datasets Improves Private Learning
Yufei Chen, Tejumade Afonja, Anvith Thudi et al. (+1 more)
Many machine learning applications involve sensitive data and therefore require training under differential
privacy (DP). However, DP training often degrades model utility. In some cases, first pre-training the model on "public" data before finetuning with DP on the sensitive data can reduce the drop in utility. However, the success of this depends on how relevant the selected public
dataset is to the sensitive data. We introduce the first pipeline that privately learns the mixture of several public
datasets to pretrain on for a given sensitive downstream task. Our key insight is that we can privately find the best mixture of multiple public
datasets by privately learning a low-dimensional linear model. We tested our method on the NIH
dataset for X-ray classification and the ENRON email
dataset for
language modeling. Applying our method to find tailored mixtures of X-ray
datasets to pretrain on for diseases in the NIH ChestX-ray14
dataset, we improved macro AUC by up to 0.037 across
privacy budgets compared to the baselines, with gains as large as +22.8% relative AUC on Cardiomegaly at $\epsilon=1$. For DP training on the ENRON
dataset, pre-training on our mixture of The Common Pile (a collection of public-domain text
datasets) decreased test perplexity by 16% relative to the baseline mixtures.
Read the full abstract ▾
All authors Yufei Chen, Tejumade Afonja, Anvith Thudi, Nicolas Papernot
Machine Learning · Cross-listed from stat.ML
Inverse Cross-spectral Neural Networks for Multivariate Time Series
Lorenzo Marinucci, Leonardo Di Nino, Gabriele D'Acunto et al. (+2 more)
CoVariance Neural Networks and their extensions have emerged as effective tools for processing multivariate data, deriving graph shift operators directly from second-order statistics. These architectures, however, are designed for independent and identically distributed observations and do not fully capture the joint structure of temporal and cross-variable dependencies in multivariate time series. In this work, we introduce Inverse Cross-Spectral Neural Networks (iCSNNs), a class of graph neural networks for stationary multivariate time series whose shift operators are the inverse cross-spectral density (iCSD)
matrices. These operators encode frequency-specific conditional relationships among variables, exploiting the decomposition provided by the spectral representation
theorem. Leveraging spectral smoothness, frequencies are grouped into bands sharing a single iCSD operator, yielding a compact parametrisation that retains the frequency-dependent structure of the process. We further propose a joint learning procedure to estimate both the
Fourier-domain dependence structure and the iCSNN parameters, adapting the iCSD operators to the downstream task. When tested on synthetic data, iCSNN outperforms baselines from different methodological families.
Read the full abstract ▾
All authors Lorenzo Marinucci, Leonardo Di Nino, Gabriele D'Acunto, Paolo Di Lorenzo, Sergio Barbarossa
Machine Learning · Cross-listed from cs.IT
On the Cardinality of Optimal Representations in the Binary-Source Information Bottleneck
Dier Tang and Jun Chen
The information bottleneck (IB) seeks a representation $U$ of a source $X$ that retains as much information as possible about a target $Y$, subject to a constraint on $I(U;X)$. A classical argument shows that it suffices to consider representations with at most $|\mathcal{X}|+1$ symbols, and this bound is known to be tight whenever $|\mathcal{X}| \geq 3$. We show that the binary case behaves differently: if $X$ is binary and $Y$ is finite, then for every joint distribution of $(X,Y)$ and every rate constraint, the IB optimum is attained by a binary $U$. Hence the bound $|\mathcal{U}| \leq |\mathcal{X}|+1$ sharpens to $|\mathcal{U}| \leq |\mathcal{X}|$ for binary sources. The proof combines a separating hyperplane argument with the observation that, for a binary source, the ratio of the second derivatives of the two
entropy functions involved is concave.
Read the full abstract ▾
Machine Learning
Frozen Factor or Spectral Band? Disentangling Two Choices in Low-Rank LoRA
Adnan Slimane Ali, Ayoub Belfatmi and David Ngwe Pouth
Spectral variants of low-rank adaptation (LoRA) choose both a subspace and which factor to freeze. We separate these choices by freezing the input factor A or output factor B on the top or bottom singular directions of pretrained weights, with learning rates selected separately. At rank 2, the same-band advantage of freezing A is larger than either within-factor band difference on all four task-model pairs with complete comparisons. Freezing B also trails comparable-budget free LoRA by 8-18 percentage points on five pairs spanning a formatting task and OpenBookQA. The A-frozen advantage persists in a single-GPU-model replication and within individual MLP module groups, including controls with equal or greater trainable counts for B frozen, and when A is frozen on a random orthonormal basis. The factor contrast weakens with rank. On OpenBookQA / Qwen2.5-1.5B at rank 16, PEFT's MiCA implementation trails comparable-budget LoRA by 3.08 points under a shared training recipe transferred from the MiCA paper. A trained oracle output subspace largely removes the low-rank deficit; partial warm-up gains recur across three direction seeds. The factor-versus-band ordering is descriptive; an approximate multiplicity audit weakens several earlier significance claims. These results extend known factor asymmetry by showing how its magnitude depends on spectral placement, rank and training conditions.
Read the full abstract ▾
Machine Learning · Cross-listed from cs.CV
RealtimeWAM: One-Step Asynchronous World Action Models
Chengtao Lv, Jinyang Du, Shuyi Feng et al. (+7 more)
World Action Models (WAMs) incorporate visual representations from
video generation backbones to guide action prediction. Recent efficient WAMs adopt Mixture-of-
Transformers (MoT) architectures and compute
video representations once for reuse by the action expert. However, intra-expert iteration (\ie, multi-step action denoising) and inter-expert waiting (\ie, sequential execution of the
video and action experts) still limit inference efficiency. To this end, we present RealtimeWAM, an extremely efficient WAM variant with one-step action generation and asynchronous inference, addressing these two bottlenecks. To reduce intra-expert iteration, we propose Teacher-Anchored Consistency
Distillation (TACD) to address a local-global error gap: low local consistency error alone does not guarantee accurate final actions. TACD supplements local consistency with explicit supervision from the frozen teacher's multi-step rollout endpoint, enabling accurate one-step action generation. Additionally, we propose Cross-Expert Wavefront Pipelining (CEWP) to eliminate unnecessary expert-level waiting. It overlaps the two experts through block-wise sharing of the
video KV cache, synchronizing only immediately before the corresponding action
attention consumes it. Extensive experiments across diverse
benchmarks (\eg, LIBERO, LIBERO-Plus and RoboTwin) and model variants (\eg, Fast-WAM and Faster-WAM) demonstrate the superiority of RealtimeWAM. Notably, RealtimeWAM maintains near-lossless performance (\ie, $<1\%$ drop) across these
benchmarks while delivering significant end-to-end speedup (\eg, $\sim25\times$ on H100). Our code and checkpoints are available via this \href{https://github.com/ModelTC/LightX2V/tree/main/examples/realtimewam}{link}.
Read the full abstract ▾
All authors Chengtao Lv, Jinyang Du, Shuyi Feng, Yang Yong, Shiqiao Gu, Shunzi Yang, Ruihao Gong, Shen Ren, Tianwei Zhang, Wenya Wang
Machine Learning · Cross-listed from stat.ML
The Surrogate Is Not the Reward: Post-Surrogate Primary-Outcome Acquisition in Contextual Bandits
Kyungbok Lee and Michael R. Kosorok
We study contextual bandits in which a surrogate is observed after the action but before the learner decides whether to acquire the primary outcome that defines action value and regret. The value of acquiring the primary outcome depends on both decision relevance (how much the current outcome matters for comparing policies) and the residual uncertainty after observing the surrogate. The Audited Surrogate Bandit (ASB) learns a contextual policy while allocating a budget of $B$ primary-outcome acquisitions over $T$ rounds. ASB sets a pre-surrogate acquisition level from current decision relevance and, after observing the surrogate, redistributes that level using an estimate of that residual uncertainty. For a finite class of $N$ policies over $K$ actions, ASB incurs $\widetilde O[\sqrt{KT\log N}\{1+\sqrt{T/B}\}]$ regret relative to the best policy in the class. In a two-action family where the surrogate does not reveal the better action, a learner that observes the surrogate before deciding whether to acquire can achieve bounded regret, whereas any learner that must decide before seeing the surrogate incurs $\Omega(T/B)$ worst-case regret under the same budget. Synthetic experiments show that both acquisition factors matter: ASB has lower regret than variants using only decision relevance or only residual uncertainty. On a KuaiRec
benchmark of user-
video interactions, the regret gap relative to relevance-only acquisition widens and then narrows as the budget grows.
Read the full abstract ▾
Machine Learning
Separators Make Carry Propagation Learnable:The Geometry of Latent Carry in a Multiplication Transformer
Sama Satariyan, Raphael Cousin and Gérard Biau
Transformers asked to multiply multi-digit numbers in a single forward pass often fail, and interpretability studies of pretrained
language models find arithmetic solved by input-range heuristics rather than by an explicit carry. We train small Llama-style
transformers from scratch on 4x4 multiplication without chain of thought and find that the input format is decisive: inserting a space
token between digits raises exact-match accuracy from 1% to 89%. Output positions are learned in carry-chain order, with the middle digits, which have the longest-range dependencies, learned last. Inside the model, the separator
token that predicts each digit (its prediction slot) encodes the carry-in as an angle on a ring in the residual stream; examples with more distinct carry values fill more of the ring. Activation patching between examples matched on the column sum shows that this state is causally used before the last layer: patching the prediction slot alone transfers the source carry in up to 84% of cases after block 4 for one middle column of our best model, while for other columns the carry is first assembled at the neighboring answer slot before reaching its own. Remaining errors are almost always off by one, consistent with a small error on the carry or on the circular digit code.
Read the full abstract ▾
Machine Learning · Cross-listed from cs.AI
Mind the Execution Gap: Action-Semantic Mismatch in World-Model Control
Shengtao Wen, Xiang Chen, Yu Tian et al. (+3 more)
World-model controllers rely on action-conditioned dynamics for prediction and planning, yet real control systems often execute commands asynchronously due to communication delay, packet loss, reordering, and actuator buffering. We study how asynchronous execution changes the action semantics assumed within world-model controllers, rather than treating it only as an external control disturbance. Through controlled interventions, we identify two architecture-dependent failure modes: planning-based controllers such as TD-MPC2 suffer from a future-action timeline mismatch between imagined and executed action sequences, while recurrent world models such as DreamerV3 can attribute observed transitions to commands that were not actually applied. Our analysis shows that TD-MPC2 requires the correct future action sequence during latent dynamics rollout, whereas DreamerV3 requires timely attribution of each transition to the action that generated it. Based on these findings, we introduce two lightweight execution-consistent interfaces, Future-Sequence for TD-MPC2 and Applied-Action Feedback for DreamerV3, that correct these mismatches without modifying the pretrained world models. Experiments across delays, packet loss, reordering, multiple control domains, measured network traces, and a process-separated asynchronous stack consistently support both diagnoses and the corresponding architecture-specific corrections.
Read the full abstract ▾
All authors Shengtao Wen, Xiang Chen, Yu Tian, Lingbing Guo, Lina Gong, Sheng-Jun Huang
Machine Learning
LinearPFN: Amortized Variable Selection for Linear Models with Interactions
Louis Schiekiera, Max Zimmer, Christophe Roux et al. (+3 more)
Spike-and-slab regression is a standard
Bayesian formulation of variable selection: it returns a posterior distribution over which candidate effects are active rather than a single selected subset, so that every candidate effect carries an inclusion probability. Its cost grows exponentially with the number of candidate effects, so the posterior can be enumerated exactly only when the number of predictors is small. Beyond that reach, the posterior has to be approximated, typically by Markov chain Monte Carlo over the model space, which requires a fresh run for every
dataset and, within a fixed budget of steps, may fail to converge. We present LinearPFN, a prior-data fitted
transformer network that amortizes spike-and-slab inference for linear models with main effects and pairwise interactions. The network is pretrained once on synthetic
datasets, drawn from an explicitly specified prior, and a single forward pass over a new
dataset returns posterior inclusion probabilities, posterior-mean coefficients and posterior predictive distributions with no per-
dataset fitting. The prior is conjugate by design, so that the posterior for each fixed set of active effects has a closed form, and wherever the exact posterior is still computable by enumeration we verify the network's outputs against it. On real predictor
matrices from published social-science
datasets, with outcomes drawn from the prior so that the true active set is known, LinearPFN attains a higher per-
dataset selection AUC and a higher F1 under the median probability model rule than five classical baselines. The lead holds when the coefficients, the interactions or the noise depart from the prior. Code: https://github.com/schiekiera/LinearPFN. Trained model: https://huggingface.co/schiekiera/LinearPFN.
Read the full abstract ▾
All authors Louis Schiekiera, Max Zimmer, Christophe Roux, Manuel Arnold, Sebastian Pokutta, Fritz Günther
Machine Learning · Cross-listed from cs.GT
MIRT: Transformers for Truthful Generative Auctions with Whole-feed Permutation Externalities
Ali Elahi, Ermis Soumalias, Jason Cheuk Nam Liang et al. (+2 more)
Modern online platforms commonly rank ads and organic content separately before blending them into a feed displayed to the user, overlooking externalities: an item's click-through rate depends on its surrounding content, not only on its own position. Recent learning-based feed generation mechanisms model some of these cross-type interactions to globally optimize for the whole feed's welfare. However, these approaches either fix the ordering of organic content, or lack exact strategyproofness guarantees for bidders. To combat these shortfalls, we introduce the Maximal-in-Range
Transformer (MIRT) mechanism class, which uses a
transformer to generate a range of candidate feeds that jointly order ads and organic content, and selects the welfare-maximizing feed in the range. However, there is a tension: strategyproofness requires the generated range to be bid-independent, even though a candidate feed's welfare depends linearly on the bids. Our key technical contribution is a reinforcement learning approach that incorporates both candidate generation and bid-aware selection into training, enabling a bid-independent
transformer to learn to generate high-welfare ranges by accounting for both individual feed quality and the collective quality of the range. Additionally, we bound the pseudo-dimension of the MIRT class under hard
attention, showing that near-optimal expected welfare is learnable with sample complexity
polynomial in the
transformer size and only logarithmic in the range size. Empirically, MIRT outperforms the previous non-strategyproof state-of-the-art feed models while remaining exactly strategyproof. Our results show that
transformer-based auctions can deliver externality-aware whole-feed optimization without sacrificing exact incentive compatibility, removing a major obstacle to their practical deployment.
Read the full abstract ▾
All authors Ali Elahi, Ermis Soumalias, Jason Cheuk Nam Liang, Daniel Yao, Michael J. Curry
Machine Learning
Empirical Variational Autoencoder
Kaede Shiohara
We present Empirical Variational Autoencoder, a general generative framework for continuous-valued (i.e., non-vector-quantized) sequences. EVA is based on the evidence lower bound of the Variational Autoencoder (VAE) but learns autoregressive latent priors empirically from training data, which can be implemented only by an additional single linear layer on top of VAEs. By replacing the conventional standard-Gaussian constraint with the self-predicted priors, EVA significantly alleviates the latent distribution gap between prior and posterior which is typically observed in conventional VAEs, and leads to high-fidelity ancestral sampling for sequential data generation. Extensive experiments on image and sound synthesis demonstrate that EVA achieves competitive generation quality with autoregressive
diffusion baselines despite its much faster inference time.
Read the full abstract ▾
Machine Learning
A Fine-Grained Analysis of the LoRA Fine-Tuning Landscape with Implications for Data Selection
Bowen Zhang, Changrui Fang, Xinsong Ma et al. (+2 more)
Low-Rank Adaptation (LoRA) has become a standard approach for parameter-efficient fine-tuning, yet a fundamental practical question remains unresolved: how should the adapter rank be chosen? An overly small rank may lead to a poorly conditioned optimization landscape, whereas an unnecessarily large rank sacrifices the efficiency that motivates LoRA in the first place. Existing theoretical analyses provide only limited guidance on this trade-off, and their guarantees are typically established under restrictive theoretical settings. We address this gap by developing a substantially sharper landscape theory for LoRA, building on modern results from nonconvex low-rank
matrix sensing. Our central insight is that the appropriate adapter rank should depend on the quality of the data-induced optimization geometry, rather than on the model alone. To formalize this connection, we introduce LoRA-RIP, a data-dependent restricted-isometry metric that characterizes the conditioning of the cross-
entropy (CE) objective along LoRA-relevant low-rank directions. We prove that sufficient rank over-parameterization, with the required rank explicitly determined by the LoRA-RIP constant, eliminates spurious local minima, thereby extending existing RIP-based guarantees beyond the classical 1/3 regime. This characterization further enables principled data selection under a fixed rank budget. Experiments across
language and
vision tasks support these theoretical predictions, showing that rank and data quality are two coupled resources that should be jointly considered for more efficient and reliable LoRA fine-tuning.
Read the full abstract ▾
All authors Bowen Zhang, Changrui Fang, Xinsong Ma, Jiaye Teng, Ziye Ma
Machine Learning
WaveGSSM: Graph Wave State Space Models for Propagating Spatio-Temporal Patterns
Junyou Zhu, Fenying Cai, Ping Xiong et al. (+5 more)
Spatio-temporal graph models typically encode each snapshot with a GNN and then connect the resulting representations through a temporal module. This space-then-time design is effective, yet it does not explicitly represent how a pattern moves across the graph. We show empirically that, for a propagating process, the same present field can lead to different futures when its recent rate of change differs, motivating an explicit representation of motion in the predictive state. We introduce WaveGSSM, a second-order graph state-space model that maintains two coupled latent states at each node, one for the current pattern and one for its temporal rate of change. A graph-wave transition updates the motion state through graph interactions and uses it to advance the pattern state, coupling spatial propagation and temporal
evolution within a single rollout. We evaluate WaveGSSM on four temporal-graph
benchmarks and global weather forecasting. It consistently achieves the best mean performance across the temporal-graph
benchmarks and reduces the geopotential RMSE by 20.2% on average for 1- to 5-day weather forecasts relative to a backbone-matched snapshot model, while better preserving large-scale atmospheric patterns.
Read the full abstract ▾
All authors Junyou Zhu, Fenying Cai, Ping Xiong, Christian Nauck, Langzhou He, Chao Gao, Jürgen Kurths, Frank Hellmann
Machine Learning · Cross-listed from cs.CL
Anatomy of LLM Sycophancy: What a Flip Rate Hides
Haonan Huang
A model under pushback can correct itself, capitulate, or hold, and one flip rate counts a correction and a capitulation alike. Using SycoLens, a modular replay protocol, we test how user pressure and evaluation settings shape measured flip rates. Each measurement is one stateless replay of an item, a committed answer, and one scripted user line in a fixed form. Every effect is read against a matched control with the line deleted. Pushback wording, committed text, answer format, boundary distance, and ground truth become factors of one instrument; earlier instruments vary one to three of them. Across eleven frontier models from three providers and about 760,000 controlled replays, which models look sycophantic depends on how the user pushes back. Lines that assert the opposite verdict and lines that challenge the answer without asserting one rank the models almost unrelatedly. Flip effects grow several-fold near a model's boundary, yet items answered identically in every screening draw still carry about half of the most-affected totals. On arithmetic tasks where the truth is known, one model re-derives and corrects itself under pressure while another abandons correct answers without written work. On the model tested, a planted derivation lowers release of the answer it argues for, true or wrong, where a bare stated value does not; the wrong answer is corrected much more often than the true one is abandoned. Under a yes/no readout the rankings come closer, entangled with a pressure-induced shift toward "no". One score per model therefore compares different behaviours across models and
benchmarks. We condense these dependencies into a reporting profile; the instrument, records, and analyses will be released upon publication.
Read the full abstract ▾
Machine Learning
Conditional Flow Matching for Single-Neuron Electrophysiology: Capturing Multimodal Responses Across Stimuli
Cameron Schofield, Luca Ghafourpour, Philip H. Wong et al. (+2 more)
Neurons of the
brain exhibit a rich repertoire of electrophysiology dynamics with the same repeated stimulus eliciting very different voltage responses from the same
cell. One common approach in biophysically detailed models is to capture this variability through ensembles of deterministic parametrizations, at a cost of hundreds of thousands of CPU hours. Existing machine learning surrogates inherit the same limitation, where a stimulus is mapped to a single voltage response. We address this challenge by learning a conditional generative model for single-
neuron electrophysiology, using flow matching with a velocity field conditioned on the input current. On biophysically detailed models of two human cortical interneuron types, the generated responses closely reproduce the electrophysiological feature distributions, spike-time structure, and excitability profiles, even matching the experimental recordings from the corresponding human cortical
neurons. Near the firing threshold, firing and non-firing responses coexist at the same stimulus amplitude, and at high amplitudes, ensembles may split into low- and high-firing modes near depolarization block. We show that our model recovers both modes in each case, while a neural operator baseline suppresses spiking near threshold and blurs the gap between modes at depolarization block.
Read the full abstract ▾
All authors Cameron Schofield, Luca Ghafourpour, Philip H. Wong, Costas A. Anastassiou, Richard E. Turner
Machine Learning · Cross-listed from cs.AI
ANT: A Multi-Granularity Network Traffic Dataset and Benchmark for Agents Behavior Auditing
Fan Li, Xiangyu Gao, Zixuan Liu et al. (+4 more)
The growing adoption of large
language model (LLM)
agents creates a need for network administrators and security teams to audit
agent behavior within organizational networks without inspecting private user content. Network traffic offers an observable source of evidence, but how much it reveals about
agent tasks and operations remains unclear. Existing traffic
datasets lack the joint task and stage annotations needed to evaluate this question. We introduce ANT (
Agent Network Traffic), a
dataset providing
agent behavior information at risk, scenario, and behavior primitive granularities alongside network traffic. ANT contains 3,114 execution episodes across 20 tasks and five scenarios, comprising 276,417 bidirectional flows and 40,049 behavior primitive segments organized into 47 macro groups. We establish a
benchmark for
agent risk identification, scenario recognition, and behavior primitive classification using 13 representative traffic analysis baselines. The results show that existing methods recover useful but uneven behavioral signals. They struggle to identify risk when malicious workflows resemble benign tasks and to distinguish scenarios with similar traffic patterns. Primitive classification is more reliable for frequent macro groups and those with distinctive traffic patterns than for rare or semantically similar groups. ANT provides a common basis for developing more precise auditing and forensic analysis of
agent behavior from network traffic. Our data and code are available at https://anonymous.4open.science/r/ant-main-suite-7BC0/.
Read the full abstract ▾
All authors Fan Li, Xiangyu Gao, Zixuan Liu, Tong Li, Chuanpu Fu, Ziqiang Wang, Ke Xu
Machine Learning · Cross-listed from cs.CL
SOL: Measuring Gaps between Text Distributions by Double Sliced Wasserstein Metrics
Gregor Kornhardt, Moritz Piening, Jannis Chemseddine et al. (+1 more)
Evaluating text generation requires measuring how well the generated distribution matches the data distribution. For autoregressive models, this is done by the perplexity.
Diffusion and flow-based
language models can only provide a likelihood bound, whose tightness differs between model families. Sample-based substitutes such as generative perplexity with
entropy do not consider the distribution fit. We propose SOL, a distance between text distributions. Each sequence is represented by the empirical measure of its hidden states under a fixed
transformer and the distributions of these measures are compared by the double sliced Wasserstein distance. We prove that SOL is a metric if the
transformer is injective. Experiments show that SOL detects distributional failures, recovers expected model trends, and provides stable sample-based estimates. We put forward SOL to fill the gap in the current evaluation protocol used for non auto-regressive models. As a first step we use SOL to re-evaluate a variety of models trained on OpenWebText.
Read the full abstract ▾
All authors Gregor Kornhardt, Moritz Piening, Jannis Chemseddine, Gabriele Steidl
Machine Learning
Xaurora: Generative Weather Forecasting with Denoising Stochastic Interpolants from a Foundation Model Prior
Eliot Walt, Miltiadis Kofinas, Nikolaj Mücke et al. (+2 more)
Deep learning has revolutionised weather forecasting in recent years, especially through atmospheric foundation models, which offer competitive skill for a fraction of the computational costs of classic physics-based models. However, most existing foundation models are deterministic, limiting the generation of large ensembles for accurate uncertainty quantification, extreme weather risk assessment, and long-range weather forecasting. Furthermore, these models incur a large, often prohibitive, computational overhead to train from scratch. To address these shortcomings, we turn a pretrained deterministic prior model, namely the Aurora foundation model, into a generative ensemble-prediction model. To that end, we introduce a novel generative method, Denoising
Stochastic Interpolants, combined with a replay buffer for
Stochastic Differential Equation (SDE) rollout, enabling probabilistic training of SDE trajectories. Our
stochastic foundation model, Xaurora, is finetuned from the small Aurora version, yet it approaches the state-of-the-art on global ensemble metrics and is competitive with the large version of Aurora. Our method is parameter and sample efficient, and generates skilful 15-day forecasts in 13 minutes. Our results demonstrate that deterministic foundation models can be efficiently extended into even stronger
stochastic models.
Read the full abstract ▾
All authors Eliot Walt, Miltiadis Kofinas, Nikolaj Mücke, Efstratios Gavves, Dim Coumou
Machine Learning
Improving Proactive AI Assistance with Hierarchical Procedural Understanding
Jin-Seop Lee, TaeYeon Won, SeongJun Jung et al. (+5 more)
Proactive AI assistants continuously observe a user's activity and decide whether to provide new guidance or remain silent. They should provide appropriate guidance for the task, determine when to provide the next guidance based on task progress, and adjust the guidance level to the user's expertise and needs. Supporting these capabilities requires training and evaluation data that reflect procedural structure and capture how guidance should adapt to task progress and user needs. However, existing
datasets either focus on detection-based proactive understanding or provide procedural guidance at a fixed granularity. Fixed-granularity guidance provides limited information about fine-grained progress and broader procedural context, making it difficult to determine completion and adapt guidance granularity. To address these limitations, we introduce the ProactiveCoach suite, comprising ProactiveCoach-Instruct for training, ProactiveCoachBench for evaluation, and fine-tuned VLMs with an adaptive guidance system. ProactiveCoach-Instruct provides hierarchically structured guidance at the phase, step, and action levels for learning task progress and procedural context. ProactiveCoachBench evaluates whether models provide appropriate guidance at the right time across different guidance levels and adapt when the requested level changes. We fine-tune pretrained VLMs on ProactiveCoach-Instruct and demonstrate its effectiveness across backbones. Compared with fixed-granularity supervision, hierarchical supervision improves overall performance across backbones by up to 9.6%p. We further build an adaptive guidance system by combining our fine-tuned model with a lightweight guidance router. Without additional fine-tuning, our system outperforms the in-context adaptation baseline by 57.1%p across four guidance-level transitions. Our project page is available at https://jinsuby.github.io/ProactiveCoach/.
Read the full abstract ▾
All authors Jin-Seop Lee, TaeYeon Won, SeongJun Jung, JungHoon Kim, Boyang Albert Li, JinYeong Bak, Jaehong Yoon, Jee-Hyong Lee
Machine Learning · Cross-listed from cs.CV
NeuroCBIR: A Fast and Accurate Image Retrieval System for Whole-Brain and Region-Specific MRI
Felix Nieto-del-Amor, Jingru Fu, J.-Sebastian Muehlboeck et al. (+3 more)
Content-based image
retrieval (CBIR) in neuroimaging enables the identification of structurally similar
brain scans, supporting diagnosis, prognosis, and treatment planning; however, existing methods are often limited to small
datasets, single
brain regions, or coarse class labels, thereby restricting their clinical utility and generalizability. Here, we present NeuroCBIR, a framework for fast and flexible
retrieval of both whole-
brain and region-specific 3D T1w MRI scans. A total of 103 cortical and subcortical regions are extracted to enable both whole-
brain and region-level queries. NeuroCBIR leverages latent representations learned by a variational autoencoder (VAE) combined with contrastive learning, producing scan-specific
embeddings that capture anatomical patterns. These
embeddings were evaluated for subject re-identification, zero-shot age prediction, and zero-shot multi-class pathology stratification. Re-identification performance was high across both whole-
brain and
brain-region levels (mean average precision across the top-5 retrieved images (mAP@5) >= 98.4%), with robust generalization across
datasets and acquisition conditions. While NeuroCBIR is not trained for age prediction or pathology stratification, zero-shot evaluations for these two tasks demonstrate that the
embeddings encode meaningful information for downstream tasks.
Embedding extraction on a 4-core CPU required approximately 18.7 s per scan, whereas similarity search was effectively instantaneous (less than 0.01 s). NeuroCBIR is publicly available for
brain MRI with more than 26,000 precomputed T1w MRI
embeddings. It supports reproducible research, region-specific flexibility, and clinically meaningful personalized diagnostic support. The software is available at https://github.com/minnelab/NeuroCBIR.
Read the full abstract ▾
All authors Felix Nieto-del-Amor, Jingru Fu, J.-Sebastian Muehlboeck, Eric Westman, Daniel Ferreira, Rodrigo Moreno
Machine Learning
FairProp: Fair Node Representation Learning via Differentiable Propagation Layers
Emmanouil Kariotakis and Aritra Konar
Graph neural networks (GNNs) are the standard tool for node representation learning and are increasingly used in high-stakes settings. Their message-passing backbone, however, can amplify topological bias, raising
fairness concerns. We study group
fairness at the level of downstream predictions for node classification, link prediction, and node regression, and bound the demographic parity gap for an arbitrary number of sensitive groups. Our node classification bound is provably no looser than the closest prior result. For link prediction, ours is the first bound on the parity gap of the deployed sigmoid-activated prediction rather than a pre-activation proxy, and for node regression we provide the first such bound. Across all three tasks, the analysis identifies two distinct sources of bias: the separation between group means and the within-group covariance of the final representations. Building on this insight, we embed
fairness into propagation itself by augmenting the convex smoothing problem underlying APPNP with a convex group-mean constraint and a within-group covariance regularizer. Unfolding projected gradient descent on this problem yields FairProp, whose layers pair a propagation step with a closed-form projection and which provably converges linearly to the unique fair optimum. Experiments on three tasks show that FairProp, even with exact group-mean equalization alone, provides a strong inductive bias that achieves excellent
fairness-utility trade-offs against strong baselines.
Read the full abstract ▾
Machine Learning
Latent Flow Matching for Molecular Graph Generation
Mathis Goupillon, Roman Bresson, Konstantinos Divriotis et al. (+1 more)
Modern graph generative models typically operate directly in the discrete graph space, explicitly generating node and edge variables, which can become costly as graphs grow. In this paper, we perform generation explicitly on latent representations of entire graphs obtained from a pretrained Variational Autoencoder with high reconstruction fidelity. The generated representations, obtained through flow matching, are then decoded only at the final step. Across molecular
benchmarks of increasing size, our approach achieves strong validity and FCD while offering a favorable quality-efficiency trade-off compared with state-of-the-art explicit graph generative models. One of the main advantages of this formulation is that the graph representation only needs to be learned once, after which the same one can be reused across multiple generative objectives without retraining. We demonstrate generation guided by molecular properties and further introduce validity-aware generation though a classifier learned directly in latent space. All code will be made available upon acceptance.
Read the full abstract ▾
All authors Mathis Goupillon, Roman Bresson, Konstantinos Divriotis, Michalis Vazirgiannis
Machine Learning
A Physics-Guided Transformer Framework for Electromigration Analysis in Multi-Segment Interconnects
Pavlos Stoikos, Anuj Pathania and George Floros
As technology scales to smaller nodes, increasing current densities make electromigration (EM) one of the dominant reliability challenges in on-chip interconnects. Accurate transient stress analysis is needed to identify wires susceptible to EM degradation, but applying physics-based solvers across many interconnects remains computationally expensive. This paper proposes a physics-guided
transformer framework for fast EM stress prediction in multi-segment interconnect lines. The framework converts each line into geometry- and DC-aware segment
tokens and uses
transformer attention to capture line-level context. A lightweight query decoder then predicts stress at selected locations and time instants. The model is trained with an objective that combines normalized supervised regression, linewise relative-$L_2$ loss, and physics-guided continuity and terminal-flux terms. Experiments on IBM power grid
benchmarks show that the proposed model achieves relative-$L_2$ error below 8% and reaches up to 2459.68$\times$ speedup compared with the
matrix exponential~solver.
Read the full abstract ▾
Machine Learning
EMG-FM-Bench: A Comprehensive Benchmark for Foundation Model Transfer and Adaptation on Electromyography
Tianhao Wu, Jiawei Yu, Xiangdong Hu et al. (+8 more)
Foundation models (FMs) are increasingly being developed for general time series and physiological signals, yet their transferability to downstream physiological tasks remains poorly understood. This question is particularly challenging for electromyography (EMG), where signal distributions vary substantially across users, sensing configurations, acquisition hardware, and downstream tasks. We introduce EMG-FM-Bench, a systematic
benchmark for studying foundation-model transfer and adaptation on EMG. EMG-FM-Bench unifies 20 public
datasets with over 1 million EMG segments and evaluates nine pretrained foundation models across four questions: how pretrained models perform when frozen or fully fine-tuned, how much pretraining helps compared with training the same model from scratch, how well models generalize to new users with limited labeled data, and how performance changes across different EMG tasks. Across the
benchmark, linear probing provides useful information about pretrained representations, but full fine-tuning can substantially change downstream EMG performance. Comparing each pretrained model with the same model trained from scratch shows that the benefit of pretraining varies substantially across models and is not universal. Performance decreases when models are evaluated on new users, while five-shot adaptation improves macro-F1 in 70.2% of evaluated model-
dataset combinations but recovers only part of the lost performance. Model performance is highly consistent between upper- and lower-limb classification and remains strongly correlated with continuous EMG-to-text decoding. Together, these results provide a systematic view of when pretrained time-series models transfer effectively to EMG and how their performance depends on fine-tuning, user variation, and downstream task.
Read the full abstract ▾
All authors Tianhao Wu, Jiawei Yu, Xiangdong Hu, Xu Wu, Hongyu Wu, Sidharth Anupkrishnan, Amirmohammad Radmehr, Yi Chen Liu, Phuc Nguyen, Yi Wu, Jian Liu
Machine Learning
Time-series Foundation Models for Predictive Control: The Role of Excitation
Mazen Amria, Jasper Hoffmann, Philipp Bordne et al. (+5 more)
Deploying model predictive control (MPC) requires constructing or identifying a predictive model for each target system. Time-series foundation models (TSFMs) offer an attractive option thanks to strong zero-shot forecasting capabilities across systems. However, low forecast error does not guarantee that a TSFM captures the system's response to the alternative actions considered by the controller. We study this gap using residential heat-pump control as a test bed, measuring the agreement between predicted and ground-truth effects of control interventions. Importantly, we find that TSFMs can recover the system's input-response relationship when the context contains sufficient independent control excitation. Common fine-tuning pipelines and feature smoothing reduce, but do not eliminate, the need for in-context excitation. Our results indicate that current TSFMs used for predictive control require sufficiently informative control variation in the inference context. Initial closed-loop results show promise for shorter context windows.
Read the full abstract ▾
All authors Mazen Amria, Jasper Hoffmann, Philipp Bordne, Anna Rothenhäusler, Lilli Frison, Harald Taxt Walnum, Sebastien Gros, Joschka Bödecker
Machine Learning
ARO: Aligned Representation learning for multi-Omics data
Amogh Singh, Yash Shah, Chiara D'Ercoli et al. (+4 more)
The high cost of functional molecular assays, and prevalence of missing modalities and unmatched samples in computational biology, create significant barriers to comprehensive multi-omic profiling, essential for capturing and
reasoning over molecules,
cells, tissues, and organisms. This work proposes a model that learns meaningful representations from multi-omics cancer data supporting the reconstruction of missing and unpaired modalities. Contrary to increasingly complex, larger models, e.g. Foundation Models (FMs), ARO prioritizes practical applicability in limited or incomplete data settings. ARO optimally reconstructs missing modalities (MSE of $0.15$ on the validation and test data in the Unmasked settings), with its learned latent
embeddings enabling a downstream cancer classification task. Our findings indicate that analyzing diverse molecular layers as a single integrated system offers a reliable and cost-efficient approach, reducing dependence on large-scale experimental testing, while still supporting multi-omic exploration in limited data settings.
Read the full abstract ▾
All authors Amogh Singh, Yash Shah, Chiara D'Ercoli, Arash Mehrjou, Patrick Schwab, Timothy Jones, Pietro Liò
Machine Learning
Better Call Reward: Reward Hacking as Strategic Abstention in Legal Reasoning Models
Subramanyam Sahoo and Justin Shenk
What happens when a legal AI model learns to look like a lawyer instead of
reasoning like one? We fine tune Qwen3-8B with Group Relative Policy Optimisation (GRPO) against a proxy built from three surface features: citation count, legalese density, and response length. The model does not learn to reason more effectively. It learns to withhold commitment. Across 16 yes or no legal
reasoning tasks from LegalBench (N=320), overall accuracy collapses from 0.500 (chance) to 0.072 (McNemar p < 10^-36), driven entirely by the rate of properly formatted answers falling from 0.900 to 0.109. The model stops committing to answers. Yet when it does commit, accuracy rises from 0.556 to 0.657, showing that the collapse is not a failure of capability but a strategic response: the model has learned that verbose responses packed with citations but empty of a direct answer score higher than terse correct ones. We term this the Saul Goodman effect, a policy that becomes maximally lawyerly while becoming maximally noncommittal, and prove formally that it is the optimal response to any surface feature proxy that attaches no penalty to abstention. We further show that 89.3% of citations produced after training are structurally implausible
hallucinations, many of them subtly corrupted names of real landmark cases, constructed in effect to survive a casual read and fail under scrutiny. To detect this failure mode before deployment, we introduce three diagnostic tools: the Confidence Theater Score (CTS), the Citation Plausibility Rate (CPR), and the Regret Gap (RG). In a domain where a confidently wrong answer can constitute malpractice, the broader lesson is direct: a reward function that measures how legal a response looks will produce a model that is maximally photogenic and minimally useful.
Read the full abstract ▾
Machine Learning
HeuFouFT: Task-Guided Metaheuristic Coordinate Search for Fourier Fine-Tuning
Ruiheng Wang, Yubo Hou, Yakun Zhu et al. (+3 more)
We introduce Heuristic-Guided
Fourier Fine-Tuning (HeuFouFT), a task-guided framework for selecting trainable frequency coordinates in
Fourier fine-tuning. Existing uniform and Gaussian band-pass schemes allocate a limited spectral budget through fixed, task-agnostic rules. HeuFouFT instead searches for coordinates using downstream performance. A coarse intensity map from lightweight block-level probes initializes three metaheuristic optimizers: Genetic
Algorithm with Simulated Annealing (GA-SA), Particle Swarm Optimization (PSO), and Cuckoo Search (CS). During search, a Random Forest filters each population so that only the top 30% of candidates proceed to proxy fine-tuning. On E2E with GPT-2-Medium, all three variants outperform random-uniform FourierFT, Gaussian band-pass FourierFT, and LoRA across five metrics. PSO further outperforms LoCA, the best-performing baseline, on four metrics while using 37.6% fewer trainable spectral coefficients. Once coordinates are selected, HeuFouFT requires only 15–18% FLOPs of Full FT. These results show that task-guided search allocates limited spectral capacity more effectively than fixed sampling. Our code is publicly available.
Read the full abstract ▾
All authors Ruiheng Wang, Yubo Hou, Yakun Zhu, Tianle Shen, Tao Wan, Zengchang Qin
Machine Learning · Cross-listed from stat.ML
KESurv: A Kernel Ensemble Method for Patient-Specific Survival Prediction
Rahul Goswami
Predicting patient-specific survival functions is crucial for clinicians in making informed decisions about patient care and treatment strategies. Among the various models available, the Survival Forest has demonstrated significant effectiveness in numerous scenarios. In this work, we propose an ensemble method that leverages the strengths of the Survival Forest as the master model, complemented by several base models. This ensemble incorporates the Beran
estimator, a type of kernel
estimator, to enhance predictions of patient-specific survival curves. We evaluated the performance of our proposed model using four distinct healthcare
datasets. The results highlight the superiority of our ensemble method over baseline models in both calibration and ranking across most
datasets. The findings suggest that our approach offers a more accurate and reliable estimation of patient-specific survival functions, providing a valuable tool for clinical decision-making.
Read the full abstract ▾
Machine Learning · Cross-listed from stat.ML
Valid Stopping in Adaptive Generator-Verifier Loops
Mahmoud Hegazy, Michael I. Jordan and Aymeric Dieuleveut
Numerous agentic workflows are based on a generator-verifier loop: a generator proposes candidates, a cheap verifier scores them, and the workflow terminates when a proposal is verified as good enough. The verifier typically proxies a more costly ground-truth oracle, and as the generator searches adaptively against it, false acceptances may accumulate. Proposals can pass the proxy but fail under the costlier ground-truth check. We study when to stop these loops while controlling the false discovery rate of the accepted proposals. Our construction introduces tools of independent interest in distribution-free statistical testing and conformal risk control, including analysis of $e$-values constructed through index betting and a novel conformal risk control procedure for non-monotone losses. We validate the approach in synthetic settings and on a
protein-design
benchmark.
Read the full abstract ▾