Search: “models”

BALAR : A Bayesian Agentic Loop for Active Reasoning

arXiv:2605.05386v2 Announce Type: replace-cross Abstract: Large language models increasingly operate in interactive settings where solving a task requires multiple rounds of information exchange with

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Beyond Selection: Token Parameterization for Extreme Visual Token Compression

DGX agent

arXiv:2609.35232v1 Announce Type: new Abstract: Visual-token compression is effective for improving the efficiency of vision-language models, but under extreme compression budgets, token pruning can b

model-releasesarxiv-cs-cv

29 Sept 2026

Beyond Token Savings: A Systematic Study of Context Compression in LLM Agents

arXiv:2609.32961v1 Announce Type: new Abstract: As LLM agents tackle longer tasks, they increasingly compress growing histories of reasoning, actions, and tool outputs. Compression can reduce token us

Can't Find Waldo: Evaluating VLMs' Sensitivity to Image Resolution and Detail Level

arXiv:2609.31706v1 Announce Type: new Abstract: Visual Language Models (VLMs) have achieved remarkable success across diverse tasks, yet they struggle with high-resolution inputs where critical inform

CFLoRA: Federated Fine-tuning of LLMs with Complementary Factors for Error-free Aggregation

arXiv:2609.33147v1 Announce Type: new Abstract: Federated low-rank adaptation (LoRA) enables collaborative fine-tuning of large language models without centralizing private client data. Its factorized

Local Ai

ChestPheNoT: Deployable, Auditable Label-Status-Evidence Extraction from Radiology Reports

arXiv:2609.31629v1 Announce Type: cross Abstract: Structured phenotype extraction from radiology reports supports cohort construction, quality auditing, and clinical analytics, but practical deploymen

local-aiarxiv-cs-lg

CLIMB-flow: Coupled Linear Inverse posterior sampling via Multiscale-Based flow

arXiv:2609.33834v1 Announce Type: new Abstract: Diffusion models are now widely used in Bayesian inverse problems in imaging as priors, where latent diffusion models are often used for larger scale pr

Collaborative Principle Evolution via Evidence Transfer for Scientific Discovery

arXiv:2609.35315v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents promise to automate scientific discovery, yet exploring the vast hypothesis space remains costly. Existing princ

ColNanoVDR: Document-Free Query Distillation for Multi-Vector Visual Document Retrieval via Optimal Transport

arXiv:2609.34899v1 Announce Type: cross Abstract: Multi-vector retrievers built on vision-language models lead visual document retrieval (VDR), but they run a multi-billion-parameter query encoder on

Continuous Variational Synthesis

arXiv:2609.35083v1 Announce Type: cross Abstract: Biological machine learning was long bottlenecked by the ability to synthesize designed DNA. Variational synthesis models control chemical reactions t

Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning

arXiv:2502.02332v3 Announce Type: replace-cross Abstract: We study task selection to enhance sample efficiency in model-agnostic meta-reinforcement learning (MAML-RL). Traditional meta-RL typically as

Correction-space Cross-variate Interaction for Test-time Adaptation in Time Series Forecasting

arXiv:2609.34638v1 Announce Type: new Abstract: Test-time adaptation (TTA) is a promising paradigm for handling distribution shift in time-series forecasting (TSF), where models adapt at inference tim

Curating Merchant-Matching Training Data with Two Confidence-Gated Local LLM Judges

arXiv:2609.33878v1 Announce Type: cross Abstract: Merchant matching resolves a noisy payment descriptor to a retrieved merchant entity or returns no match. A key challenge in curating training labels

D-GAP: Improving Out-of-Domain Robustness via Dataset-Agnostic and Gradient-Guided Augmentation in Frequency and Pixel Spaces

arXiv:2511.11286v3 Announce Type: replace Abstract: Out-of-domain (OOD) robustness is challenging to achieve in real-world computer vision, especially in unsupervised domain adaptation scenarios, wher

DepthBench: Measuring How Residual Connections Enable More Computational Depth

arXiv:2609.32534v1 Announce Type: new Abstract: Depth is a natural way to increase the computational capacity in Transformers, yet the contribution of deeper layers can diminish as depth grows larger.

DimPO: Dimensionality Reduction for Attention using Preference Optimization

arXiv:2609.32579v1 Announce Type: new Abstract: A linear projection can reduce the dimension of query and key vectors without updating the pretrained model, but it remains unclear which training objec

Local Ai

Direct Hidden-State Alignment: Mapping and Controlling Preference Expression in LLMs

arXiv:2609.33298v1 Announce Type: new Abstract: In many settings, post-training need not create the target behavior from scratch: the base model can already produce it, but not reliably. This shifts p

local-aiarxiv-cs-lg

Direct Self-Evolving Optimization: Evolving LLMs without Challenger Training

arXiv:2609.34279v1 Announce Type: new Abstract: Self-evolving language models improve by generating tasks and learning from their own feedback, but adapting the task generator often requires a separat

Distribution-Conditioned Task Routing for Class-Incremental Learning

arXiv:2609.34503v1 Announce Type: new Abstract: Parameter-efficient adaptation enables continual learners to acquire task-specific knowledge through compact model updates while maintaining strong with

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Does Adversarial Training Improve Generalization in Multi-View VLAs? Revealing and Mitigating View Collapse

arXiv:2609.33707v1 Announce Type: cross Abstract: Vision-language-action (VLA) models adapt pretrained vision-language models (VLMs) for closed-loop robot control, transferring their perceptual and se

safetyarxiv-cs-ro

DP-Rec: Towards Dynamic Patching for Efficient Long-Sequence Recommendation

arXiv:2609.32215v1 Announce Type: new Abstract: Transformers have redefined sequential recommendation by effectively modeling dynamic user behaviors and long-range dependencies. However, they remain i

DynamicDx: Evaluating Evidence Acquisition in Video-Based Diagnosis

arXiv:2609.32957v1 Announce Type: new Abstract: Diagnosing a patient from video requires more than recognizing the sign: a vision-language model must turn what it sees into hypotheses, questions and t

EEGAgentBench: Benchmarking LLM Agents on Short- and Long-Horizon EEG Analysis

arXiv:2609.31632v1 Announce Type: new Abstract: Electroencephalography (EEG) analysis is evolving from short-segment classification toward long-horizon interpretation that demands iterative evidence a

Evaluating Single and Multi-Omics Based Explainable Artificial Intelligence (MOXAI) for Molecular Subclass Classification of Adult-Type Diffuse Gliomas

arXiv:2609.32190v1 Announce Type: new Abstract: DNA methylation (DNAM) profiling has emerged as a powerful diagnostic tool for classifying brain and solid tumors. However, existing computational model

Local Ai

Federated 3D Gaussian Splatting for Large-Scale Scene Reconstruction at Wireless Edge

arXiv:2609.32177v1 Announce Type: new Abstract: Three-dimensional (3D) Gaussian splatting (3D-GS) has emerged as a promising technique for large-scale scene reconstruction due to its high rendering ef

local-aiarxiv-cs-cv

From Grey-Box to Green-Box: When can Physics-Informed Machine Learning Reduce Carbon Footprints in Structural Health Monitoring?

arXiv:2609.33387v1 Announce Type: new Abstract: Machine learning plays an increasingly vital role in engineering, but the corresponding increase in compute time is not without environmental cost. Phys

From HL to H+L-1 Parameters: A Hankel-Toeplitz Forecaster for Long-Term Time Series Forecasting

arXiv:2609.33984v1 Announce Type: new Abstract: Linear forecasters have shown competitive accuracy against Transformer-based models in long-term time series forecasting. We study how classical station

From One-Shot Generation to Incremental Music Composition: Adapting a General-Purpose Instruction LLM for Persistent Symbolic Editing

arXiv:2609.34994v1 Announce Type: cross Abstract: Most music-generation systems are still framed and evaluated primarily as producers of complete outputs, whereas composition often proceeds through su

From Phase Transition to Systemic Failure: A Decoupled Analytics Framework for GNN Robustness

arXiv:2609.31656v1 Announce Type: new Abstract: Data quality is a major bottleneck for the reliable deployment of graph neural networks (GNNs) in real-world graph mining tasks. Among various sources o

Identifying Temporal Features within Transcoders for Time Sensitive Factual Recall

arXiv:2609.33183v1 Announce Type: cross Abstract: Large Language Models (LLMs) suffer from temporal misalignment, often due to the contradictory nature of their training corpora. While current mitigat

Improving Test-Time Scaling with Adaptive Looped Transformers

arXiv:2609.35748v1 Announce Type: cross Abstract: Looped transformers have demonstrated promising parameter efficiency by reusing layers for latent computation. Prior studies compare looped and non-lo

Investigating the Effect of k-NN Preprocessing on Developing Graph Neural Networks: A Fairness-Based Perspective

arXiv:2609.33416v1 Announce Type: new Abstract: In this paper, a methodology to design fair graph convolutional neural networks (GCNs) is developed and tested over several application data sets. The g

Jev in Medicine: A Benchmark Evaluation. Preliminary Results

arXiv:2609.34024v1 Announce Type: cross Abstract: Jev is a non-generative 'System One' model that assigns probabilities to predefined answer options and cannot answer outside them. Its accuracy and ca

Learning Steadily: Accumulating Relative Point Margin Scores for Face Image Quality Assessment

arXiv:2609.31662v1 Announce Type: new Abstract: Face Image Quality Assessment determines the suitability of captured face images for automated face recognition (FR), a critical capability for reliable

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

Many Eyes, One World: Feed-Forward 3D Reconstruction from Mixed Cameras

arXiv:2609.35658v1 Announce Type: new Abstract: Real-world capture is heterogeneous: perspective, fisheye, and 360^irc panoramic images can coexist within a single reconstruction task, yet most feed-f

Masking Frequent Tokens Sharpens Direct Preference Optimization

arXiv:2609.32445v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) aligns language models by optimizing over sequence-level sums of token-wise implicit reward differences. However,

MetaSampling: Making Frame Samplers Efficient for Long-Video Question Answering

arXiv:2609.33998v1 Announce Type: new Abstract: Frame selection is an important component of long-video question answering (VQA) with Multimodal Large Language Models (MLLMs). Existing frame-selection

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MinkowskiPE: Minkowski Positional Encoding for Spatiotemporal Perception

arXiv:2609.33804v1 Announce Type: cross Abstract: Modeling spatiotemporal coupling is a key challenge in building physical intelligence across scales, from microscopic to macroscopic. Existing models

safetyarxiv-cs-cv

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

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

On-Policy Self-Distillation for Multi-Turn Image Editing

arXiv:2609.35611v1 Announce Type: new Abstract: Instruction-based image editing has achieved strong performance in single-turn settings, yet practical editing is often iterative, with each instruction

P^2O: Joint Policy and Prompt Optimization

arXiv:2603.21877v4 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) enhances Large Language Model (LLM) reasoning but is suffer from advantage collapse: when all

Perceptual Quality Loss or Loss of Perceptual Quality?

arXiv:2609.35054v1 Announce Type: cross Abstract: Contemporary deep speech enhancement (SE) models are often trained with specific auxiliary terms in the loss function as a way to improve their perfor

Poly-attention: a general scheme for higher-order self-attention

arXiv:2602.02422v2 Announce Type: replace Abstract: The self-attention mechanism, at the heart of the Transformer model, is able to effectively model pairwise interactions between tokens. However, num

Precise Editing and Flexible Referencing for Interactable Worlds

arXiv:2609.34470v1 Announce Type: new Abstract: We present EditWorld, a video world model for precise editing and flexible referencing in interactable worlds. Existing video world models primarily foc

Principal Steering Subspaces for Online Adaptation of Frozen Generative Robot Policies

model-releasesarxiv-cs-ro

arXiv:2609.33765v1 Announce Type: new Abstract: Generative robot policies provide expressive behavior priors, but updating a large diffusion or flow-matching model through online interaction is costly

Query, Align, and Distill: Navigation-Aware Cross-Modal Interaction for Efficient Vision-and-Language Navigation

arXiv:2609.33097v1 Announce Type: new Abstract: Recent large-scale Vision-and-Language Navigation (VLN) models deliver strong accuracy but remain costly to deploy due to their large parameter counts a