Search: “inference”

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

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Model Releases

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

DGX agent

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

model-releasesarxiv-cs-lg

29 Sept 2026

OMP-MoE: Efficient Expert Pruning for Mixture-of-Experts LLMs via Orthogonal Matching Pursuit

arXiv:2609.31631v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models enable efficient scaling of large language models but face critical deployment challenges due to massive memory requirem

Tutorials

Optimal Transport Dropout for Structured Predictive Uncertainty

arXiv:2609.33377v1 Announce Type: new Abstract: Deterministic neural networks and neural operators provide point predictions with no intrinsic measure of reliability. Yet, predictive uncertainty may s

tutorialsarxiv-cs-lg

Panoptic Scene Program Diffusion Transformer

arXiv:2609.31780v1 Announce Type: new Abstract: Modern text-to-image models produce high-fidelity images but still struggle with compositional prompts that require instance identity, attribute ownersh

researcharxiv-cs-cv

PQR3D: Progressive Query Refinement over Reference-Conditioned Temporal Windows for Multi-View 3D Object Detection

arXiv:2609.32163v1 Announce Type: new Abstract: Temporal context is essential for camera-only multi-view 3D object detection. Existing streaming detectors maintain and propagate query states from one

researcharxiv-cs-cv

ProtoSeam: Lifting Classifier Training with Latent Gaussian Mixture Models

arXiv:2609.35174v1 Announce Type: new Abstract: We propose a lifted reformulation of supervised classification that improves the final accuracy of standard classifiers without changing the architectur

Q-learning Penalized Transformer for Safe Offline Reinforcement Learning

arXiv:2609.34426v1 Announce Type: new Abstract: This paper addresses the problem of safe offline reinforcement learning, which involves training a policy to satisfy safety constraints using an offline

RefCompose: Multi-Reference Image Generation via LoRA-Conditioned Diffusion

arXiv:2609.32389v1 Announce Type: new Abstract: Filmmakers and visual artists routinely need to compose multiple references, actors, locations, props, cultural elements, into a single coherent shot, b

local-aiarxiv-cs-cv

ReSight-SMC: Two-Stage Power Sampling via Island SMC with Visual Scouts

arXiv:2609.34905v1 Announce Type: new Abstract: Power sampling has emerged as a training-free approach to LLM reasoning, eliciting capabilities comparable to reinforcement learning by sharpening the m

ReVision3D: Attribution-Guided Recursive Self-Improvement for 3D Medical Perception

arXiv:2609.32984v1 Announce Type: new Abstract: Recursive self-improvement (RSI) offers a promising path for overcoming the limited visual capability of current medical imaging agents. Yet applying RS

researcharxiv-cs-cv

SolveEdit: Benchmarking Visual Problem Solving in Generative Models

arXiv:2609.35504v1 Announce Type: new Abstract: Machine intelligence is often evaluated through abstract reasoning problems, yet many real-world problems are visual, such as arranging objects, repairi

Tutorials

SphMind: Towards Robust, Training-Free VLM-based Spatial Reasoning with a 360 Camera

arXiv:2609.33462v1 Announce Type: new Abstract: Omnidirectional or 360 cameras provide embodied AI agents with a holistic, wide field-of-view (FoV) view of their surroundings, motivating the use of Mu

tutorialsarxiv-cs-cv

Synthetic Thermal Image Generation for Real-Time Animal Detection Under Low-Visibility Conditions

arXiv:2609.32944v1 Announce Type: new Abstract: Wildlife-vehicle collisions remain a significant road safety concern, particularly during nighttime and low-visibility conditions when RGB-based percept

safetyarxiv-cs-cv

Tools

Together is #1 on @OpenRouter token share across top open coding models, today: 🥇 @Zai_org GLM 5.3 Flash: 29.2% 🥇 @deepseek_ai V4.1 Flash:…

Together is #1 on @OpenRouter token share across top open coding models, today: 🥇 @Zai_org GLM 5.3 Flash: 29.2% 🥇 @deepseek_ai V4.1 Flash: 25.6% 🥇 @Kimi_Moonshot Kimi K3: 18.9% Running coding agents o

toolstogether-ai--x

UnfoldCRF: Structured Mask Refinement with Image-Conditioned Latent Regions

arXiv:2609.33996v1 Announce Type: new Abstract: Learned mask refiners improve segmentation accuracy, but it is hard to tell how much of the improvement comes from explicit structure rather than from e

Applications

What Makes World Action Models Generalize? An Empirical Study of Test-Time Future Modeling

arXiv:2609.34981v1 Announce Type: new Abstract: World action models (WAMs) predict the future alongside actions during training. Due to the heavy computation cost of video denoising, whether the futur

applicationsarxiv-cs-cv

When World Models Lie: Adaptive Safety Analysis Under Wrong Imaginations

arXiv:2609.34300v1 Announce Type: new Abstract: World models offer a powerful substrate for safety reasoning in high-dimensional robotic systems, but they are also fallible: their predictions can be b

safetyarxiv-cs-ro

95+ TPS through 100K generated for qwen3.8 27b, 262K ctx, on a single 3090

model-releasesr-localllama

Hello everyone! A little while back I posted about LlamAmpere, a fork of Llama.cpp with Ampere-specific improvements (though it is caught up to main and will support other hardware, too). Thank you to

Actively Resolving Contextual Uncertainty for Underspecified Tasks in Natural Language

arXiv:2609.30428v1 Announce Type: cross Abstract: Foundation models provide robots with the ability to interpret natural language and reason about environmental context, yet most language-conditioned

safetyarxiv-cs-ai

Adaptive Random Matrices in Gaussian Bandits: Spectral Universality and Selection-Induced Outliers

arXiv:2609.30321v1 Announce Type: cross Abstract: Adaptive arm selection changes the distribution of the observations collected by a bandit algorithm, but it need not change their limiting empirical s

safetyarxiv-cs-lg

Benchmarking Attention for Tabular Foundation Models

arXiv:2609.31306v1 Announce Type: new Abstract: Tabular in-context learners such as TabPFN, Mitra, or ConTextTab rely on alternating row and column attention over 2D sequences of latent embeddings. Th

Beyond Empirical Support: Structured Outlier Generation via Sinkhorn Optimal Transport

arXiv:2609.31470v1 Announce Type: cross Abstract: Outliers are essential for evaluating and improving the robustness of machine learning systems, especially when future distributions may differ signif

Brenier Meets Adversarial Training: Optimal Transport Geometry for Robust Learning

arXiv:2609.31363v1 Announce Type: new Abstract: Distributionally robust optimization (DRO) provides a principled framework for learning under distribution shift, but its practical use is hindered by t

Budgeted Quotient-Residual Guidance for Frozen Pocket-Conditioned Molecular Diffusion

arXiv:2609.31222v1 Announce Type: new Abstract: Pocket-conditioned molecular diffusion updates ambient atom coordinates, but many lead-optimization objectives are expressed on quotient features such a

local-aiarxiv-cs-lg

Can You Check That? The Checkability Boundary for Local LLM Network Automation

arXiv:2609.31540v1 Announce Type: cross Abstract: Sending every network-automation input to a third-party frontier LLM exports sensitive artifacts such as production configurations, topologies, and lo

local-aiarxiv-cs-ai

Hardware

Compress What You See, Not What You Say: Anchored Context Distillation for Latent-Observation Software Engineering Agents

arXiv:2609.31430v1 Announce Type: new Abstract: Tool observations dominate the context of software-engineering agents, making long interaction histories costly to maintain. Existing context compressio

hardwarearxiv-cs-ai

Applications

Cost-Aware Best-LLM Identification using Dueling Feedback

arXiv:2609.30360v1 Announce Type: cross Abstract: Inspired by the problem of identifying the best model from a collection of large language models (LLMs) with heterogeneous querying costs, we formulat

applicationsarxiv-cs-ai

Counterfactual Online Conformal Prediction Under Adaptive Logging

arXiv:2609.30811v1 Announce Type: new Abstract: Online conformal prediction can fail when predictions shape actions and actions determine which outcomes enter calibration. Standard adaptive methods ma

safetyarxiv-cs-lg

CSCWD: Cross-Scale Channel-wise Knowledge Distillation for Lightweight Tiny Object Detection on Edge Devices

arXiv:2609.30395v1 Announce Type: new Abstract: Real-time tiny object detection in aerial imagery is constrained by the weak spatial evidence of very small objects and the loss of high-resolution deta

Feeding BabyLMs Macaroni: Code-Switching Curricula Cause Cross-Lingual Convergence

arXiv:2609.30535v1 Announce Type: new Abstract: Children in multilingual communities often code-switch, using multiple languages in a single utterance. Can we induce cross-lingual alignment in languag

safetyarxiv-cs-cl

G^2PTQ: Improving LLM Post-Training Quantization with Generalized Gradient Compensation

arXiv:2609.31009v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is a practical approach to reducing the memory and computational footprint of large language models (LLMs) without re

local-aiarxiv-cs-ai

Highlight-Then-Summarize: Learning to Compress Evidence for Long-Context Understanding

model-releasesarxiv-cs-ai

arXiv:2609.31382v1 Announce Type: cross Abstract: Long-context understanding requires large language models (LLMs) to reason over lengthy documents, conversations, and code, yet task-relevant evidence

HyperErase: Scale-Calibrated Hypernetwork for Multi-Concept Erasure in Text-to-Image Models

arXiv:2609.31154v1 Announce Type: new Abstract: Recent advances in text-to-image (T2I) generation have substantially improved visual synthesis, but have also raised increasing safety concerns due to t

HySTAR: Anchored Hypergraphs for Stable Credit Assignment in Cooperative Multi-Agent Reinforcement Learning

arXiv:2609.31531v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning under partial observability and shared rewards requires assigning team outcomes to individual agents and

Low-Rank Friction for Memory-Efficient Transformer Pretraining

arXiv:2609.30342v1 Announce Type: cross Abstract: iKFAD is a recently proposed optimiser that replaces adaptive learning rates with adaptive friction in the momentum dynamics, yet performs as well as

LUMO (Lightweight Unified Multilingual Orchestrator): A Privacy Preserving Offline Voice Assistant

arXiv:2609.30692v1 Announce Type: new Abstract: Reliable voice interaction is essential in environments with limited internet connectivity and strong privacy. However, most existing voice assistants d

local-aiarxiv-cs-lg

Agents

Momentum-Guided Federated Split Distillation for Personalized Temporal Edge Intelligence

arXiv:2609.31159v1 Announce Type: new Abstract: We propose a momentum-guided federated split distillation framework for personalized, efficient, and autonomous temporal edge intelligence. We introduce

agentsarxiv-cs-ai

More Sensors Only One Field: Rethinking Continual Spatio-Temporal Forecasting

arXiv:2609.31325v1 Announce Type: new Abstract: Continual spatio-temporal forecasting supports traffic management and environmental monitoring under evolving dynamics and expanding sensor networks. Ho

Applications

PhoenixSR: Generative Heterogeneous Distillation Unleashes Efficient Models for Real-World Super-Resolution

arXiv:2609.30988v1 Announce Type: new Abstract: Real-world image super-resolution (SR) requires recovering perceptually realistic high-resolution images from complex low-resolution observations while

applicationsarxiv-cs-cv

Prompt-Based Continual Compositional Zero-Shot Learning

model-releasesarxiv-cs-ai

arXiv:2512.09172v3 Announce Type: replace-cross Abstract: We tackle continual adaptation of vision-language models to new attributes, objects, and their compositions in Compositional Zero-Shot Learnin

SLMFix: Leveraging Small Language Models for Domain Specific Language Error Fixing with Reinforcement Learning

arXiv:2511.19422v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown impressive capabilities in code generation across many programming languages but even state-of-the-art

researcharxiv-cs-ai

Spackle: Completing Large View Single Image NVS with Adaptive Gaussians

arXiv:2609.30941v1 Announce Type: cross Abstract: Single-image novel view synthesis (NVS) enables photorealistic rendering of un- observed viewpoints from a single input. Practical NVS systems require

researcharxiv-cs-ai

Stepwise Intrinsic Rewards for Reasoning in Large Language Models

arXiv:2602.01034v2 Announce Type: replace Abstract: Reinforcement learning (RL) has become a widely used paradigm for improving the reasoning abilities of large language models (LLMs) and Vision-langu

safetyarxiv-cs-ai

The Residual Stream's Effective Depth

arXiv:2609.31098v1 Announce Type: new Abstract: We introduce effective depth (Deff), a scalar diagnostic that treats the layer-wise residual stream of a transformer as a discrete-time process, measure

The Uncontrolled Variable: Vision-Language Refusal Is Conditioned on the Image-Attachment Interface, and Not Robust to Irrelevant Image Properties

arXiv:2609.26174v2 Announce Type: replace-cross Abstract: We show that aligned vision-language models also condition refusal on a property of a request's form: whether an image is attached, holding ev

safetyarxiv-cs-ai

Uncertainty and Explainability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach

arXiv:2609.31570v1 Announce Type: cross Abstract: Deep learning has substantially accelerated the calibration of complex stochastic-volatility models, but neural point calibration alone does not captu

Up and Down the Abstraction Ladder: Code-Based Skills for Language Agents

arXiv:2609.31076v1 Announce Type: new Abstract: Language agents struggle to act and learn in environments that require long sequences of low-level actions. Code-based abstractions can make these agent

local-aiarxiv-cs-ai