Search: “research”

model-releasesarxiv-cs-lg

arXiv:2605.06834v1 Announce Type: new Abstract: Continual learning research attempts to conserve two fundamental capabilities: new knowledge acquisition and the preservation of previously acquired kno

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Automated Evaluation can Distinguish the Good and Bad AI Responses to Patient Questions about Hospitalization

DGX agent

arXiv:2510.00436v2 Announce Type: replace Abstract: Automated approaches to answer patient-posed health questions are rising, but selecting among systems requires reliable evaluation. The current gold

researcharxiv-cs-ai

11 May 2026

Automatic Image-Level Morphological Trait Annotation for Organismal Images

arXiv:2604.01619v3 Announce Type: replace-cross Abstract: Morphological traits are physical characteristics of biological organisms that provide vital clues on how organisms interact with their enviro

BalCapRL: A Balanced Framework for RL-Based MLLM Image Captioning

arXiv:2605.07394v1 Announce Type: cross Abstract: Image captioning is one of the most fundamental tasks in computer vision. Owing to its open-ended nature, it has received significant attention in the

Bayesian Attention Mechanism: A Probabilistic Framework for Positional Encoding and Context Length Extrapolation

arXiv:2505.22842v4 Announce Type: replace Abstract: Transformer-based language models rely on positional encoding (PE) to handle token order and support context length extrapolation. However, existing

Bayesian Conformal Prediction as a Decision Risk Problem

arXiv:2602.03331v2 Announce Type: replace Abstract: We propose Bayesian Conformal Prediction (BCP), a framework that combines Bayesian posterior predictive distributions with PAC-style conformal risk

Beyond Defenses: Manifold-Aligned Regularization for Intrinsic 3D Point Cloud Robustness

arXiv:2605.07590v1 Announce Type: new Abstract: Despite extensive progress in point cloud robustness, existing methods primarily improve performance through augmentation or defense mechanisms, while o

Beyond Distribution Estimation: Simplex Anchored Structural Inference Towards Universal Semi-Supervised Learning

arXiv:2605.07557v1 Announce Type: new Abstract: Semi-supervised learning faces significant challenges in realistic scenarios where labeled data is scarce and unlabeled data follows unknown, arbitrary

Beyond Reasoning: Reinforcement Learning Unlocks Parametric Knowledge in LLMs

arXiv:2605.07153v1 Announce Type: new Abstract: Reinforcement learning (RL) has achieved remarkable success in LLM reasoning, but whether it can also improve direct recall of parametric knowledge rema

Bilevel Graph Structure Learning, Revisited: Inner-Channel Origins of the Reported Gain

arXiv:2605.07577v1 Announce Type: new Abstract: Bilevel graph structure learning is widely understood to improve graph neural networks by jointly optimizing model parameters and a learned graph struct

Black-box model classification under the discriminative factorization

arXiv:2605.07878v1 Announce Type: new Abstract: Access to modern generative systems is often restricted to querying an API (the ``black-box' setting) and many properties of the system are unknown to t

Bloom Filter Encoding for Machine Learning

arXiv:2512.19991v2 Announce Type: replace Abstract: We present a method that uses a Bloom filter transform to preprocess data for machine learning. Each sample is encoded into a compact bit-array repr

Bounded Fitting for Expressive Description Logics

arXiv:2605.07452v1 Announce Type: new Abstract: Bounded fitting is an attractive paradigm for learning logical formulas from labeled data examples that offers PAC-style generalization guarantees and c

Breaking QAOA's Fixed Target Hamiltonian Barrier: A Fully Connected Quantum Boltzmann Machine via Bilevel Optimization

arXiv:2605.07473v1 Announce Type: cross Abstract: To overcome the limitations of classical partially connected Boltzmann machines and mainstream quantum Boltzmann machines (QBMs), this work extends th

Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding

arXiv:2605.06679v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over-reli

Bridging Textual Profiles and Latent User Embeddings for Personalization

arXiv:2605.06981v1 Announce Type: cross Abstract: Personalized systems rely on user representations to connect behavioral history with downstream recommendation applications. Existing methods typicall

CARMEN: CORDIC-Accelerated Resource-Efficient Multi-Precision Inference Engine for Deep Learning

arXiv:2605.06878v1 Announce Type: cross Abstract: This paper presents CARMEN, a runtime-adaptive, CORDIC-accelerated multi-precision vector engine for resource-efficient deep learning inference. The k

researcharxiv-cs-ro

CASCADE: Context-Aware Relaxation for Speculative Image Decoding

arXiv:2605.07230v1 Announce Type: cross Abstract: Autoregressive generation is a powerful approach for high-fidelity image synthesis, but it remains computationally demanding and slow even on the most

CellScientist: Dual-Space Hierarchical Orchestration for Closed-Loop Refinement of Virtual Cell Models

arXiv:2605.07335v1 Announce Type: new Abstract: Virtual Cell Modeling (VCM) requires models that not only predict perturbation responses, but also support targeted revision when predictions fail. Curr

CGF-Softmax: A Cumulant-Based Softmax Reformulation for Efficient Inference under Homomorphic Encryption

arXiv:2602.01621v3 Announce Type: replace-cross Abstract: Homomorphic encryption (HE) is a prominent framework for privacy-preserving machine learning, enabling inference directly on encrypted data. H

CLIPer: Tailoring Diverse User Preference via Classifier-Guided Inference-Time Personalization

arXiv:2605.07162v1 Announce Type: new Abstract: Personalized LLMs can significantly enhance user experiences by tailoring responses to preferences such as helpfulness, conciseness, and humor. However,

Closed-Form Last Layer Optimization

arXiv:2510.04606v2 Announce Type: replace Abstract: Neural networks are typically optimized with variants of stochastic gradient descent. Under a squared loss, however, the optimal solution to the lin

Cloud-top infrared observations reveal the four-dimensional precipitation structure

arXiv:2605.07499v1 Announce Type: new Abstract: Accurate four-dimensional (4D) precipitation information is essential for understanding the Earth's energy and water cycles, yet remains observationally

Model Releases

Cluster-level reliability for trillion-parameter models on TPUs

model-releasesgoogle-cloud-ai

Frontier AI models have redefined the unit of compute. At trillion-parameter scale, AI training requires thousands of interconnected components, orchestrated in industrial-scale deployments to operate

Code Generation and Conic Constraints for Model-Predictive Control on Microcontrollers with Conic-TinyMPC

arXiv:2403.18149v3 Announce Type: replace Abstract: Model-predictive control (MPC) is a state-of-the-art control method for constrained robotic systems, yet deployment on resource-limited hardware rem

researcharxiv-cs-ro

Comprehensiveness Metrics for Automatic Evaluation of Factual Recall in Text Generation

arXiv:2510.07926v2 Announce Type: replace Abstract: Despite demonstrating remarkable performance across a wide range of tasks, large language models (LLMs) have also been found to frequently produce o

Congrats to @thinkymachines on the release of TML-Interaction-Small and tying for the top spot on our Audio MC S2S leaderboard! 🥇 Their int…

researchsoumith-chintala--x

Congrats to @thinkymachines on the release of TML-Interaction-Small and tying for the top spot on our Audio MC S2S leaderboard! 🥇 Their interaction model scores a 43.4% APR, demonstrating an impressiv

Conservative Flows: A New Paradigm of Generative Models

arXiv:2605.06905v1 Announce Type: new Abstract: Modern generative modeling is dominated by transport from a noise prior to data. We propose an alternative paradigm in which generation is performed by

CONSIGN: Conformal Segmentation Informed by Spatial Groupings via Decomposition

arXiv:2505.14113v3 Announce Type: replace Abstract: Most machine learning-based image segmentation models produce pixel-wise confidence scores that represent the model's predicted probability for each

Consistency Regularised Gradient Flows for Inverse Problems

arXiv:2605.07907v1 Announce Type: cross Abstract: Vision-Language Latent Diffusion Models (LDMs) (Rombach et al., 2022) provide powerful generative priors for inverse problems. However, existing LDM-b

Continuous First, Discrete Later: VQ-VAEs Without Dimensional Collapse

arXiv:2605.06870v1 Announce Type: new Abstract: While many approaches to improve VQ-VAE performance focus on codebook size and utilization, the effect of dimensional collapse, where trained VQ-VAE rep

Convergent Stochastic Training of Attention and Understanding LoRA

arXiv:2605.07959v1 Announce Type: new Abstract: Transformers have revolutionized machine learning and deploying attention layers in the model is increasingly standard across a myriad of applications.

Coupling Models for One-Step Discrete Generation

arXiv:2605.07193v1 Announce Type: new Abstract: Generative modeling over discrete structures underpins applications across deep learning, from biological sequence design and code generation to large l

CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning

arXiv:2605.05732v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can acquire new capabilities through fine-tuning, but continual adaptation often leads to catastrophic forgetting

Cross-Attention and Encoder-Decoder Transformers: A Logical Characterization

arXiv:2605.07705v1 Announce Type: cross Abstract: We give a novel logical characterization of encoder-decoder transformers, the foundational architecture for LLMs that also sees use in various setting

Debiased Counterfactual Generation via Flow Matching from Observations

arXiv:2605.07665v1 Announce Type: cross Abstract: Estimating counterfactual distributions under interventions is central to treatment risk assessment and counterfactual generation tasks. Existing appr

Decoding Dynamic Visual Experience from Calcium Imaging via Cell-Pattern-Aware Pretraining

arXiv:2510.18516v3 Announce Type: replace-cross Abstract: Neural recordings exhibit a distinctive form of heterogeneity rooted in differences in cell types, intrinsic circuit dynamics, and stochastic

Demystifying Lipschitz verification: positive matrices, negative results

arXiv:2603.28113v2 Announce Type: replace Abstract: The global Lipschitz constant of a neural network is related to robustness and generalization, yet unlike in many classical models, it is not plainl

Dependence on Early and Late Reverberation of Single-Channel Speaker Distance Estimation

arXiv:2605.07694v1 Announce Type: cross Abstract: Single-channel speaker distance estimation has recently achieved centimeter-level accuracy in simulated environments, yet it remains unclear which com

Differentiable Ray Tracing with Gaussians for Unified Radio Propagation Simulation and View Synthesis

arXiv:2605.07781v1 Announce Type: new Abstract: Explicit neural representations such as 3D Gaussian Splatting (3DGS) enable high-fidelity and real-time novel view synthesis, yet optimize for alpha-com

DiffRetriever: Parallel Representative Tokens for Retrieval with Diffusion Language Models

arXiv:2605.07210v1 Announce Type: cross Abstract: PromptReps showed that an autoregressive language model can be used directly as a retriever by prompting it to generate dense and sparse representatio

DIMoE-Adapters: Dynamic Expert Evolution for Continual Learning in Vision-Language Models

arXiv:2605.07494v1 Announce Type: new Abstract: Continual learning enables vision-language models to accumulate knowledge and adapt to evolving tasks without retraining from scratch. However, in multi

DINO-MVR: Multi-View Readout of Frozen DINOv3 for Annotation-Efficient Medical Segmentation

arXiv:2605.07221v1 Announce Type: new Abstract: Adapting foundation models to medical segmentation typically requires either backbone fine-tuning or high-capacity task-specific decoders, both of which

Disambiguating 2D-3D Correspondences in Gaussian Splatting-based Feature Fields for Visual Localization

arXiv:2605.07351v1 Announce Type: new Abstract: While Gaussian Splatting-based Feature Fields (GSFFs) have shown promise for visual localization, this paper highlights that photometrically optimized G

Distributional Process Reward Models: Calibrated Prediction of Future Rewards via Conditional Optimal Transport

arXiv:2605.06785v1 Announce Type: cross Abstract: Inference-time scaling methods rely on Process Reward Models (PRMs), which are often poorly calibrated and overestimate success probabilities. We prop

Don't Get Your Kroneckers in a Twist: Gaussian Processes on High-Dimensional Incomplete Grids

arXiv:2605.08036v1 Announce Type: new Abstract: We introduce CUTS-GPR, a new method for performing numerically exact Gaussian process regression (GPR) in high-dimensional settings. The key component o

Don't Ignore the Tail: Decoupling top-K Probabilities for Efficient Language Model Distillation

arXiv:2602.20816v3 Announce Type: replace Abstract: The core learning signal used in language model distillation is the standard Kullback-Leibler (KL) divergence between the student and teacher distri

DPG-CD: Depth-Prior-Guided Cross-Modal Joint 2D-3D Change Detection

arXiv:2605.07151v1 Announce Type: cross Abstract: Urban spatial evolution is manifested not only through horizontal expansion but also through vertical structural changes. Consequently, jointly captur