News / #paper Tag Research papers 500 articles archived under #paper · RSS Sign in to follow arXiv — Machine Learning research 7d ago Symmetry-Informed Causal Partial Identification arXiv:2610.09230v1 Announce Type: new Abstract: Partial identification (PI) entails estimating bounds on causal effects by encoding different assumptions on data generation as a constrained optimization problem. Such bounds can suffice to inform policy decisions even if the… 12 arXiv — Machine Learning research 7d ago An Informational Curse of Horizon in Goal-Conditioned Policy Learning arXiv:2610.09247v1 Announce Type: new Abstract: The difficulty of learning goal-reaching policies is often attributed to a "curse of horizon" that manifests as bias accumulation in temporal-difference backups and noisy advantage estimates. In this work, we identify an additional… 17 arXiv — Machine Learning research 7d ago Efficient Best-of-N policy evaluation for inference-time alignment arXiv:2610.09250v1 Announce Type: new Abstract: Best-of-N (BoN) is a common inference-time alignment method that selects the highest-scoring response among N samples from a reference model. Evaluating BoN policies from logged data is challenging under sample-only access because… 15 arXiv — Machine Learning research 7d ago The Symbol of the Surrogate: Measuring Numerical Provenance in Neural PDE Solvers arXiv:2610.09255v1 Announce Type: new Abstract: Neural PDE surrogates are trained on numerical solver outputs that contain both physical evolution and solver-specific discretization errors. Because surrogates are also evaluated against held-out trajectories from the same solver,… 38 arXiv — Machine Learning research 7d ago Evaluating Trajectory Features for Routing Final-Layer Attention arXiv:2610.09272v1 Announce Type: new Abstract: Attention routing requires a signal that predicts the value of attention on the current prefix. We evaluate whether hidden-state extrapolation error, curvature and error change improve this prediction beyond uncertainty, one-step… 34 arXiv — Machine Learning research 7d ago Twist Flow for Inverse Problems arXiv:2610.09281v1 Announce Type: new Abstract: In Bayesian inverse problems, posterior sampling requires generating samples that are consistent with given observations while capturing the range of plausible solutions. Direct conditional generative models introduce latent noise… 10 arXiv — Machine Learning research 7d ago Self-attention summary networks for subsurface velocity-model building from common-image gathers arXiv:2610.09282v1 Announce Type: new Abstract: Common-image gathers (CIGs) contain physically meaningful information about velocity-model errors through reflector focusing and residual moveout, but in conventional imaging workflows they are typically used only as diagnostic… 7 arXiv — Machine Learning research 7d ago LeCuration: A Tiny World Model as a Data Curation Multi-Tool arXiv:2610.09285v1 Announce Type: new Abstract: Many applications of physical AI run within finite or closed physical worlds with a limited set of physical laws governing object behavior. Examples include robots working in a warehouse and agents moving around in a video game. In… 34 arXiv — Machine Learning research 7d ago Node-level Graph Neural Architecture Search Framework arXiv:2610.09297v1 Announce Type: new Abstract: In recent years, Graph Neural Networks (GNNs) and architecture search frameworks have gained extensive application in non-Euclidean data processing, attributable to their superior capacity in managing unstructured data.… 29 arXiv — Machine Learning research 7d ago Kuration SDK: Addressing the Virtual2Real Gap via Data Curation arXiv:2610.09305v1 Announce Type: new Abstract: Benchmarks for measuring the quality of action-conditioned world models are still evolving and shifting away from visual similarity-based metrics to action-semantic and physically-grounded metrics. However, for domain and… 13 arXiv — Machine Learning research 7d ago MovieSTAGE: Scene, Transition, and Global Encoding for Movie-fMRI ADHD Classification arXiv:2610.09306v1 Announce Type: new Abstract: Naturalistic movie-fMRI provides a shared, temporally structured probe of brain dynamics, yet predictive models commonly rely on whole-run functional connectivity (FC) or temporally generic representations that are not aligned with… 4 arXiv — Machine Learning research 7d ago Denoising Blocks, Not Tokens: Efficient Compressed Continuous Diffusion with Branching Token Realization arXiv:2610.09311v1 Announce Type: new Abstract: Diffusion language models (DLMs) generate text through iterative parallel refinement, offering the potential for higher throughput than autoregressive (AR) decoding. However, most DLMs still maintain one generative state per token,… 37 arXiv — Machine Learning research 7d ago Benign Overfitting under Heterogeneous Input Fusion arXiv:2610.09340v1 Announce Type: new Abstract: Benign overfitting is extensively studied when learning from a single high-dimensional input, but its behavior under heterogeneous input fusion remains largely unexplored. We study this question for minimum-norm linear… 34 arXiv — Machine Learning research 7d ago Shared Low-rank Basis Factorization for Data-free Mixture-of-Experts Compression arXiv:2610.09342v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) large language models decouple capacity from compute through sparse routing, but their large parameter count creates storage and serving challenges. We analyze three MoE compression families: expert… 28 arXiv — Machine Learning research 7d ago Multimodal LLMs Can Learn to Read Brain Signals: A Vision--Language Model for Unified Multi-Task EEG Decoding arXiv:2610.09355v1 Announce Type: new Abstract: Learning EEG representations that generalize across cognitive tasks, subjects, and recording conditions remains a key challenge in electroencephalography (EEG) decoding. Recent advances in foundation models have improved EEG… 8 arXiv — Machine Learning research 7d ago Global Exponential Convergence of Two-Layer Linear Network Training arXiv:2610.09356v1 Announce Type: new Abstract: We prove global exponential (linear) convergence with an explicit rate in the rich scaling for wide two-layer linear networks trained with smooth Polyak-Lojasiewicz predictor losses. Gradient flow in the factors closes exactly in… 6 arXiv — Machine Learning research 7d ago From Retrieval to Customer Context: Evaluating Frontier-Model Systems for Voice-of-Customer Analysis arXiv:2610.09375v1 Announce Type: new Abstract: Organizations increasingly use frontier language models to analyze customer feedback, but answer quality also depends on how that feedback is organized and made available. We define a \emph{customer context graph} as a unified… 32 arXiv — Machine Learning research 7d ago Noise, Denoise, Correct: MCMC Posterior Sampling with Diffusion Priors in Three Steps arXiv:2610.09407v1 Announce Type: new Abstract: Pretrained diffusion models are powerful priors for inverse problems, but posterior sampling under nonlinear, non-differentiable forward models remain hard. We introduce diffusion waltz, an MCMC method using SDEdit-style… 19 arXiv — Machine Learning research 7d ago Stability and Diversity of Networked Self-Consuming Generative Ecosystems arXiv:2610.09409v1 Announce Type: new Abstract: The widespread deployment of generative AI has made it increasingly difficult to distinguish synthetic content from real data. Consequently, synthetic data is inevitably incorporated into the training pipelines of future model… 34 arXiv — Machine Learning research 7d ago Shared Geometry As A Rosetta Stone: Cross-Modal Alignment Without Paired Data arXiv:2610.09411v1 Announce Type: new Abstract: Multimodal representations enable zero-shot classification and retrieval, but aligning independently trained models usually requires large amounts of paired data. Yet, the Platonic Representation Hypothesis suggests that models… 32 arXiv — Machine Learning research 7d ago Self-Consuming Generative Models with Co-Evolving Human Preferences arXiv:2610.09415v1 Announce Type: new Abstract: Generative models are increasingly trained in self-consuming iterative loops, where users curate preferred samples from model-generated candidates and the curated samples are used to train future generations of the model. Prior… 4 arXiv — Machine Learning research 7d ago What a Reporting Convention Hides: A Matched-Budget Audit of Quantum Natural Gradient with an Exactly Computed Metric arXiv:2610.09425v1 Announce Type: new Abstract: Several published comparisons of variational quantum optimizers time only runs that reach a target loss, or read the verdict at a single target. Either convention could decide whether an optimizer's costlier steps pay off. We… 4 arXiv — Machine Learning research 7d ago An extended deep energy method for thermo-mechanical crack propagation arXiv:2610.09433v1 Announce Type: new Abstract: Thermo-mechanical fracture couples transient heat conduction on a cracked domain with a crack that grows as the temperature and the displacement evolve. Neural energy solvers have been proposed for phase-field fracture and later… 33 arXiv — Machine Learning research 7d ago GeoPrior-Mamba: Structured Process Priors with Mamba for Fine-Resolution XCO2 Reconstruction arXiv:2610.09456v1 Announce Type: new Abstract: Reconstructing fine-resolution column-averaged dry-air CO2 (XCO2) fields from sparse satellite observations requires models to infer spatial structure that is only weakly constrained by direct measurements. Existing learning-based… 25 arXiv — Machine Learning research 7d ago DSReg: Provably Recovering Individual World Latents without Reconstruction arXiv:2610.09457v1 Announce Type: new Abstract: Methods that recover individual latent variables of the world, from nonlinear ICA to dictionary learning and causal representation learning, anchor the latents to observations through reconstruction, auxiliary supervision, or… 32 arXiv — Machine Learning research 7d ago When Should an In-Context Learner Expand Its Hypothesis Space? arXiv:2610.09471v1 Announce Type: new Abstract: Learning systems adapt quickly inside a familiar family of models. The harder step comes earlier: deciding, from observations that could be noise, an exception, a change within the family or structure outside it, whether opening a… 14 arXiv — Machine Learning research 7d ago MORA: Modeling Observed Changes for Drift-Robust Time-Series Anomaly Detection arXiv:2610.09473v1 Announce Type: new Abstract: Time-series anomaly detection (TSAD) identifies deviations from patterns learned from historical data. In non-stationary settings, distribution drift and true anomalies can cause similar local changes, making it difficult to tell… 34 arXiv — NLP / Computation & Language research 7d ago CHASE: Channel-Aligned Structure Exploitation for Geometry-Aware Model Engineering arXiv:2610.09476v1 Announce Type: cross Abstract: Geometric and Spectral Alignment (GSA) characterizes trained networks through spectral concentration, physical-channel alignment, support structure, and changes in singular bases. In this paper, we propose CHASE (Channel-Aligned… 19 arXiv — Machine Learning research 7d ago Safe on Average, Unsafe in the Tail: When Is the Episodic-Cost Tail Controllable? arXiv:2610.09508v1 Announce Type: new Abstract: Safe reinforcement learning seeks policies that maximize return while satisfying constraints on cumulative cost. Most methods impose these constraints on expected episodic cost. Consequently, standard evaluations report mean… 15 arXiv — Machine Learning research 7d ago Physics-Informed Neural Plasticity: PDE Solvers That Reshape Themselves arXiv:2610.09510v1 Announce Type: new Abstract: Physics-informed neural PDE solvers adapt their parameters to satisfy governing equations, yet their representational structure typically remains fixed throughout training. This rigidity is poorly matched to PDE solutions with… 26 arXiv — Machine Learning research 7d ago Differential Refresh Policies for Models Trained on Lagging Data Snapshots: From a Single-Age Equivalence Limit to an Optimal Per-Segment Allocation arXiv:2610.09519v1 Announce Type: new Abstract: Production machine-learning models are derived artifacts of time-bounded training snapshots: a deployed model is a materialized view over a training cut that ages the instant it is built. A common response is to replace the fixed… 35 arXiv — Machine Learning research 7d ago Align Before You Combine: Reference Space Calibration for Supervision Without Ground Truth arXiv:2610.09525v1 Announce Type: new Abstract: We introduce a calibration-first framework that produces supervision scores without access to ground-truth labels or a shared annotation space. Our framework aligns subset-specific scorers using a synthetic ordinal reference space… 19 arXiv — Machine Learning research 7d ago CircuitGate: Logic-Consistent Circuit-Level Functional Modeling for And-Inverter Graphs arXiv:2610.09549v1 Announce Type: new Abstract: And-Inverter Graphs (AIGs) are fundamental representations for logic synthesis and verification in Electronic Design Automation (EDA). As structured representations of complex digital systems, AIGs require models to capture… 24 arXiv — Machine Learning research 7d ago A Framework for the Systematic Review of ML Assets in AI Registries arXiv:2610.09551v1 Announce Type: new Abstract: Background: Modern software systems increasingly rely on Machine Learning (ML) assets (i.e., pre-trained models, datasets, benchmarks) for building, evaluating, and integrating ML-based systems. However, current exploration,… 14 arXiv — Machine Learning research 7d ago UniCSI Towards a Universal Wi-Fi CSI Encoder for Ubiquitous Human Sensing arXiv:2610.09559v1 Announce Type: new Abstract: Wi-Fi sensing promises to turn the everyday wireless signals that already surround us into ubiquitous sensors for human sensing. However, a fundamental obstacle is that CSI is acquired under diverse device-specific configurations,… 16 arXiv — Machine Learning research 7d ago EvoSignal: LLM-Guided Evolutionary Design of Modular Traffic Signal Control Programs arXiv:2610.09563v1 Announce Type: new Abstract: Effective traffic signal control (TSC) requires policies that respond to changing traffic demand and network conditions while meeting different control objectives. However, adapting existing strategies often involves repeated… 16 arXiv — Machine Learning research 7d ago Online Resource Allocation with an Endogenous Markov State: Fewer LP Solves Earn More arXiv:2610.09577v1 Announce Type: new Abstract: We study finite-horizon online resource allocation with i.i.d. requests and an endogenous Markov state on a finite state space: each action affects the transition of the state that governs future rewards and resource consumption.… 37 arXiv — NLP / Computation & Language research 7d ago Collaborative Reasoning Distillation via Cross-Feedback and Coherent Curation arXiv:2610.09587v1 Announce Type: cross Abstract: Reasoning capabilities are critical for advancing Large Language Models, yet current approaches either require massive computational budgets or struggle to effectively distill reasoning to smaller models. Standard distillation… 38 arXiv — Machine Learning research 7d ago COPC: Coupled Off-Policy Correction for Asynchronous LLM Reinforcement Learning arXiv:2610.09597v1 Announce Type: new Abstract: Asynchronous RL accelerates large language model post-training by decoupling rollout generation from optimization, but trains on stale trajectories. Existing methods primarily correct token-level policy mismatch through… 22 arXiv — Machine Learning research 7d ago Lightweight and Versatile Learned Optimization by Recombination of Gradient History arXiv:2610.09604v1 Announce Type: new Abstract: This paper presents a lightweight and versatile learned optimizer that dynamically recombines gradient history, represented as averages over disjoint time spans. The optimizer reduces the prediction space to one scalar coefficient… 26 arXiv — Machine Learning research 7d ago Decoupled Optimization for Teacher-Student Semi-Supervised Learning via a Pioneer Student arXiv:2610.09609v1 Announce Type: new Abstract: Semi-supervised learning (SSL) relies on two core mechanisms: self-training under the Teacher-Student (T-S) framework and joint optimization of labeled and unlabeled losses. Despite their effectiveness, we find both mechanisms… 14 arXiv — Machine Learning research 7d ago Sequential Pretraining Favors Large Models arXiv:2610.09611v1 Announce Type: new Abstract: Large neural networks often acquire capabilities that small models fail to learn. Does this stem from large models learning more representative features, or from being more robust to unaccounted-for adverse effects introduced… 12 arXiv — Machine Learning research 7d ago The Identifiability and Observability of Deep Normalized Attention arXiv:2610.09620v1 Announce Type: new Abstract: We study which parameters of deep, unmasked, single-head attention are determined by its input--output function. For known positive nonconstant real-analytic normalizers, the function generically determines the effective scores and… 4 arXiv — NLP / Computation & Language research 7d ago Tokka-Bench: Evaluating Tokenizers Across 100 Natural and 20 Programming Languages arXiv:2610.08794v1 Announce Type: new Abstract: Large language models rely on subword tokenizers whose quality varies across languages, yet no standardized multi-metric framework exists for broad comparative evaluation. We introduce Tokka-Bench, an open-source framework that… 19 arXiv — NLP / Computation & Language research 7d ago Child ASR Adaptation with Adult Retention: An Empirical Study arXiv:2610.08827v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) systems often underperform for children and non-native speakers, while adapting adult ASR models to child speech can cause adult-speech forgetting. We study child ASR adaptation with adult… 18 arXiv — NLP / Computation & Language research 7d ago When Forgetting Looks Like Improvement: Metric Masking in Streaming Diarizer Adaptation and the Price of Rehearsal arXiv:2610.08828v1 Announce Type: new Abstract: Small-data adaptation can improve speech detection while degrading speaker attribution. We study this discrepancy in a released streaming diarizer adapted on 7.5 h of two-party conversation and evaluated across six corpora.… 26 arXiv — NLP / Computation & Language research 7d ago Emo-Jev: Probabilistic Reasoning for Emotion Classification with Jev arXiv:2610.08829v1 Announce Type: new Abstract: Jev offers an alternative interface for language understanding: given an input and predefined questions, it returns probabilistic decisions rather than free-form responses. Whether this interface can support effective reasoning for… 4 arXiv — NLP / Computation & Language research 7d ago CoDR: Training-Free Confidence-Drift Remasking for Diffusion Language Models arXiv:2610.08833v1 Announce Type: new Abstract: Masked diffusion language models (MDLMs) decode by repeatedly committing tokens to masked positions, but these commitments are usually irreversible. A token chosen under sparse, partial context is kept fixed, even when later… 22 arXiv — NLP / Computation & Language research 7d ago Leveraging LLM-Generated Explanations for Detecting Emotionally Rewritten Fake News arXiv:2610.08835v1 Announce Type: new Abstract: The spread of fake news may cause severe social consequences. Existing fake news detection methods mainly focus on stylistic variations or incorporate external information such as explanations. However, news articles are often… 32 arXiv — NLP / Computation & Language research 7d ago Beyond the Sycophancy Score: How Task, Model, and Pressure Shape LLM Yielding arXiv:2610.08840v1 Announce Type: new Abstract: Large language models (LLMs) often abandon a correct answer, or endorse a user's position, once the user pushes back. This behavior, called sycophancy, is usually reported as a single rate per model, which says little about when it… 9 Page 10 of 10 · 500 articles ← Newer
