Search: “framework”

Learning to Steer, Steering to See: Unveiling the Geometry of RLVR in Large Language Models via Trainable Vectors

arXiv:2609.34344v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a key paradigm for enhancing the reasoning of large language models, yet the high dimensionality of parameter upd

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Learning Transferable Sensor Models via Language-Informed Pretraining

DGX agent

arXiv:2603.11950v2 Announce Type: replace-cross Abstract: Multimodal language models have demonstrated strong semantic understanding and reasoning over physiological and behavioral signals captured fr

safetyarxiv-cs-lg

29 Sept 2026

Learning What to Recall: Adaptive Multi-Cue Episodic Memory for World Models

arXiv:2609.34677v1 Announce Type: cross Abstract: World models predict future observations from current experience and actions, yet prediction can depend on observations seen far in the past. Episodic

Learning When to Recur: Token-Adaptive Recursion for Imbalanced Ophthalmic Domain Incremental Learning

arXiv:2609.32785v1 Announce Type: cross Abstract: Domain incremental learning is essential for adapting ophthalmic deep learning models to sequential clinical domains while preserving diagnostic exper

Learning Where and What to Restore for Composite Image Restoration

arXiv:2503.17915v2 Announce Type: replace-cross Abstract: Real-world degraded images often contain multiple co-occurring degradation types, making composite image restoration fundamentally different f

tutorialsarxiv-cs-cv

Local Ai

LEGAU: Learning Semantic Gaussian Priors for Scalable Category-level Pose Estimation

arXiv:2609.35046v1 Announce Type: new Abstract: Category-level 6D pose estimation from a single RGB-D observation is inherently under-constrained, since partial visible geometry must be interpreted to

local-aiarxiv-cs-cv

LFPO: Likelihood-Free Policy Optimization for Masked Diffusion Models

arXiv:2603.01563v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has achieved remarkable success in improving autoregressive models, especially in domains requ

LieDiscover: Adaptive Symbolic Library Construction for Explicit Open-form Symmetry Discovery

arXiv:2609.33663v1 Announce Type: cross Abstract: Discovering underlying symmetries from data has emerged as a crucial challenge in scientific discovery. Existing data-driven methods for symmetry disc

LLM Unlearning Evaluation with TRIAGE

arXiv:2609.32103v1 Announce Type: new Abstract: Large language models can memorize private or harmful information, motivating machine unlearning methods that remove targeted knowledge while preserving

LLM4Trust: Exploring the Capabilities of Large Language Models for Trust Evaluation

arXiv:2609.33521v1 Announce Type: new Abstract: Trust evaluation plays a critical role in cybersecurity by supporting risk mitigation and decision-making. A variety of trust evaluation methods have be

LLMs are General Asynchronous Agents

arXiv:2609.35427v1 Announce Type: new Abstract: Modern LLMs are increasingly capable as autonomous agents, but they follow sequential interaction cycles: read, think, reply or call tools, repeat. Many

Local Ai

LoCoVSR: Local Context Diffusion Posterior Sampling for Video Super-Resolution

arXiv:2609.32742v1 Announce Type: new Abstract: Video super-resolution (VSR) is an ill-posed inverse problem that aims to reconstruct a high-resolution (HR) video from a noisy, low-resolution (LR) ver

local-aiarxiv-cs-cv

LoopTrack: A Simple Baseline for Parameter-Efficient Transformer Tracking

arXiv:2609.33306v1 Announce Type: new Abstract: Current Transformer-based tracking methods typically stack multiple Transformer blocks with separate parameters to model interactions between the target

M3-Score: Fidelity, Memorization and Coverage as Separate Axes for Evaluating Generative Radiology Image Models

arXiv:2609.33769v1 Announce Type: new Abstract: Quantitative evaluation of generative models for radiology remains challenging. Clinically relevant structures are often small and infrequent, feature s

MAD-Guard: Controlled Study of Autoregressive Generation versus Direct Decision Interfaces for Closed Multimodal Forensic Tasks

arXiv:2609.33683v1 Announce Type: new Abstract: When should multimodal foundation models generate tokens, and when should they directly output a decision? We present MAD-Guard, a controlled study of o

MaD-RL: Matching Distributions for Calibrating LLMs with Reinforcement Learning

arXiv:2609.31644v1 Announce Type: new Abstract: Reinforcement learning (RL) is widely used in language-model post-training to maximize rewards assigned to individual model outputs, such as scores from

Maintaining Benchmarks Against Increasingly Capable Agents: Detection and Remediation of Unearned Passes

arXiv:2609.34262v1 Announce Type: cross Abstract: Agentic benchmarks guide model selection and training. Yet an agent can pass a task without demonstrating the intended capability. Such outcomes const

Making LLMs Truly Forget: Deep Unlearning by Searching, Selecting, and Severing Knowledge Paths

arXiv:2609.34442v1 Announce Type: new Abstract: While an unlearned language model may no longer recall a fact directly, the fact often remains recoverable through multi-hop reasoning over related know

MaLiang-Harness: A Programmable Path to Image and Video Generation

arXiv:2609.34309v1 Announce Type: new Abstract: Executable programs offer explicit control over how images and videos are constructed, but generating runnable code is only the beginning of visual crea

Mask2Restore: Self-Supervised Ultrasound Despeckling via Inpainting

arXiv:2609.32844v1 Announce Type: cross Abstract: Medical ultrasound (US) is inherently degraded by speckle, a granular interference pattern that is often treated as a complex form of noise in image r

Measurement-Error-Aware Causal Distributed-Lag Quantile Modeling of Indoor Air Pollution and Short-Term Lung-Function Deterioration

arXiv:2609.31646v1 Announce Type: cross Abstract: Low-cost indoor air-quality sensors could support personalized asthma prevention, but their nonlinear measurement error, delayed exposure effects, tim

Memory in the Sky: Low-Altitude Question Answering with Multi-Agent Memory Aggregation

model-releasesarxiv-cs-ro

arXiv:2609.35431v1 Announce Type: new Abstract: This paper studies low-altitude question answering (LAQA), in which distributed unmanned aerial vehicle (UAV) memories are aggregated at a ground server

Metro-WM: Long-Horizon Latent Planning with Realisable Sub-Goals

arXiv:2609.31868v1 Announce Type: cross Abstract: Model-predictive control with Joint-Embedding Predictive Architectures (JEPAs) provides a strong zero-shot goal-reaching planner, but it is only effec

tutorialsarxiv-cs-ro

MM-OPD: Towards One More Bottleneck Between Perception and Reasoning

arXiv:2609.32690v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) advance visual reasoning by strengthening both perception and reasoning, implicitly assuming a process t

mmHRI: Towards Privacy-Preserving Human-Robot Interaction with Millimeter-Wave Radar

arXiv:2609.34220v1 Announce Type: cross Abstract: Assistive robots increasingly operate in many human-centered environments and perform various human-robot interaction (HRI) tasks, such as object deli

Model-Agnostic Online Certificate-Driven Calibration for Time Series Forecasting Under Distribution Shift

arXiv:2609.31960v1 Announce Type: new Abstract: Time series out-of-distribution generalization requires forecasters to remain reliable when deployment dynamics differ from training conditions due to c

Model-Free Output Feedback Stabilization via Policy Gradient Methods

arXiv:2601.19284v3 Announce Type: replace-cross Abstract: Stabilizing a dynamical system is a fundamental problem that serves as a cornerstone for many complex tasks in the field of control systems. T

Model-Informed Safe Reinforcement Learning for Bipedal Locomotion via Step-to-Step Prediction

arXiv:2609.34486v1 Announce Type: new Abstract: Humanoid robots promise versatile mobility in cluttered, human-centric environments, but real deployment demands principled safety. Classical model-base

safetyarxiv-cs-ro

Modeling Whole-Slide Images as Dynamic Tumor Microenvironment Fields

arXiv:2609.34451v1 Announce Type: new Abstract: Due to the gigapixel-scale nature of whole-slide images (WSIs), weakly supervised WSI analysis is commonly formulated as a multiple instance learning (M

Modelling non-linear aeroelastic loads in long-span bridges with extreme learning machines

arXiv:2609.33274v1 Announce Type: cross Abstract: Accurate modelling of aerodynamic loads is essential for predicting instabilities and ensuring the safety of long-span bridges. A methodology is intro

MORPH: Self-Organising Multi-Robot Task Allocation via Neuroplasticity-Inspired Adaptive Topology

model-releasesarxiv-cs-ro

arXiv:2609.32745v1 Announce Type: new Abstract: Multi-robot task allocation (MRTA) in dynamic environments faces a fundamental tension: effective coordination requires learned structure, but that stru

MoSPR: Histology-to-Gene Expression Prediction with Morpho-Spatial Macrostates and Low-Rank Molecular Programs

arXiv:2609.34280v1 Announce Type: cross Abstract: Predicting molecular profiles from histopathology remains challenging because whole-slide images contain spatially organized, heterogeneous tissue pat

MotionSpaceFlow: Representation-Aware Flow Matching in Direct Motion Space

arXiv:2609.34190v1 Announce Type: new Abstract: Recent advances in diffusion and flow models have substantially improved text-driven human motion generation. Yet most methods generate in low-dimension

Multi-Bitwidth Quantization for LLMs Using Additive Codebooks

arXiv:2606.12876v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed across heterogeneous hardware with varying resource constraints, the ability to adaptively

Multi-Modal Non-Prehensile Estimation of Physical Parameters via Press-and-Pull Tipping

arXiv:2609.33138v1 Announce Type: new Abstract: Recovering physical properties of unknown objects through non-prehensile interaction is challenging because no single manipulation primitive reveals all

researcharxiv-cs-ro

Multi-Task Learning of Conditional Mean Operators: applications to dynamical systems and uncertainty quantification

arXiv:2609.35429v1 Announce Type: cross Abstract: Estimating conditional statistics and learning representations of a population of conditional distributions are central problems in many data-driven a

Multimodal LLMs Outperform Pathology Foundation Models in Cross-Domain Histological Similarity

arXiv:2609.32876v1 Announce Type: new Abstract: State-of-the-art pathology foundation models, trained on millions of histology tiles, can fail to preserve tissue similarity when comparisons cross slid

NanoForecast v0.5: Competitive Time Series Forecasting Through Training Pipeline Optimization

arXiv:2609.31669v1 Announce Type: new Abstract: We present NanoForecast v0.5, a 6.5M-parameter forecaster that competes with models 31x its size (TimesFM, 200M parameters) after training pipeline fixe

Natural State-Prediction Accuracy can Hide Weak Controlled Responsiveness in VLA Readouts

arXiv:2609.34684v1 Announce Type: cross Abstract: Accurately decoding object states from the internal representations of vision-language-action (VLA) models does not establish that the predictions res

NavJev: Efficient Vision-Language Navigation via Action-Centric Visual Compression and Discriminative Action-Semantic Memory

arXiv:2609.34969v1 Announce Type: new Abstract: Recent zero-shot Vision-and-Language Navigation (VLN) methods increasingly rely on multimodal large language models (MLLMs) to reason over visual observ

researcharxiv-cs-ro

Neural Proposals, Symbolic Guarantees: Neuro-Symbolic Graph Generative Modeling

arXiv:2602.16954v3 Announce Type: replace Abstract: While deep generative models excel at capturing graph data distributions, they struggle to satisfy complex, hard constraints. In unconstrained setti

Neural Scaling Laws of Transformer Operator Network

arXiv:2609.33533v1 Announce Type: cross Abstract: Transformers have emerged as powerful architectures for learning solution operators of physical systems. Empirically the prediction error has been obs

Neural Tractability via Structure: Learning-Augmented Algorithms for Graph Combinatorial Optimization

arXiv:2511.19573v2 Announce Type: replace Abstract: Neural solvers provide fast solutions to graph combinatorial optimization problems, but training alone does not guarantee solution quality. Exact me

Agents

NeuronDiscover: Agent-in-Twin for Mechanistic Discovery in Neuronal Microenvironments with World Action Models

arXiv:2609.35338v1 Announce Type: new Abstract: Mechanistic discovery in neuronal microenvironments requires interventions and measurements that separate competing explanations of solute transport and

agentsarxiv-cs-lg

Not All Rollouts Are Worth Learning: On Trajectory Valuation for Post-Training Reinforcement Learning

arXiv:2609.35072v1 Announce Type: new Abstract: We consider the problem of trajectory valuation in reinforcement learning: how to identify and mitigate detrimental trajectories during online training.

tutorialsarxiv-cs-lg

NutriVision: Ingredient-Conditioned Fusion and Prediction for Single-Image Food Nutrition Estimation

arXiv:2609.33076v1 Announce Type: new Abstract: Nutrition estimation is a fundamental task in consumer diet tracking, clinical dietetics, chronic disease management, sports and hospital nutrition, and

Observability-Informed Optimal Sensor Placement for Soft Robots

arXiv:2609.33681v1 Announce Type: new Abstract: This paper presents the application and experimental evaluation of a systematic method for optimal sensor placement in soft robots. Existing methods eit

tutorialsarxiv-cs-ro

OmniFysics-Captioner Technical Report: Grounding Omni-Modal Understanding in the Physical World for Better Captioning

arXiv:2609.31714v1 Announce Type: new Abstract: Building omni-modal models with physical intelligence requires fine-grained supervision that captures physical evidence such as contact, support, deform