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Traditional and Recent Approaches for Infrared Small Target Detection : Overview, Challenges and Perspectives (Part 1)

arXiv:2610.18276v1 Announce Type: new Abstract: Infrared small target detection is often used in different civilian and military applications to detect static or moving targets in scenes like in groun

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Train for Many, Update with One: Improving Multi-Sample Reasoning via Fenchel-Bregman Posterior Learning

DGX agent

arXiv:2610.17735v1 Announce Type: new Abstract: Multi-sample reasoning measures candidate coverage, the probability that at least one of K candidates is correct. However, minimizing the average loss o

researcharxiv-cs-lg

14 Oct 2026

Local Ai

Train LoRA directly on Quantized GGUFs, no PyTorch, no Parent(FP16) needed, just your GGUF and the data

I've seen this one floating around quite a bit now and since this is something that can be done without the parent and or other dependencies i thought i'd share something. Usually when you fine-tune a

local-air-localllama

Training-Free Dense Hand Contact Estimation with Multi-Modal Large Language Models

arXiv:2605.05886v2 Announce Type: replace Abstract: Dense hand contact estimation requires both high-level semantic understanding and fine-grained geometric reasoning of human interaction to accuratel

Training-Free Multi-Agent Cooperative Embodied Search from Uncertain Vision-Language Priors

arXiv:2610.15038v1 Announce Type: new Abstract: Real-world Vision-Language Navigation (VLN) requires agents to act under partial observability, where map structure, semantic cues, and target locations

safetyarxiv-cs-ro

Training Generalist Navigation Policies via Action Self-Distillation

arXiv:2610.17593v1 Announce Type: cross Abstract: Language provides a natural interface for humans to specify and reason about goals, but training language-conditioned robotic policies and reasoning t

Training LLMs for Multi-Agent Collaboration with Verifiable Information Rewards

arXiv:2610.17893v1 Announce Type: cross Abstract: Effective collaboration among large language model (LLM) agents in multi-agent systems requires sharing relevant evidence, requesting missing informat

TrajCFG: Token-Wise Classifier-Free Guidance via Denoising Trajectory Feedback

arXiv:2610.14153v1 Announce Type: new Abstract: Classifier-free guidance (CFG) typically applies a shared scale to all latent tokens, although their velocity predictions evolve differently during samp

Tree Appreciator

model-releasessimon-willison

Tool: Tree Appreciator Another game experiment with Claude. This one used the default Claude iPhone app (chat mode, not Claude Code mode) where I prompted Claude Opus 5.5 Max with: Build a three js ga

Tri-path DINO: Feature Complementary Learning for Remote Sensing Multi-Class Change Detection

arXiv:2603.01498v3 Announce Type: replace Abstract: In remote sensing imagery, multi-class change detection (MCD) is crucial for fine-grained monitoring, yet it has long been plagued by feature ambigu

tutorialsarxiv-cs-cv

Triage-PiEvo: Evidence Admission for Principle-Evolving Scientific Agents

arXiv:2610.14683v1 Announce Type: new Abstract: Large language model (LLM)-based AI Scientists advance scientific discovery, yet they suffer from inefficiencies due to noisy observations. Existing pri

TRIG: Toward Measuring Texture Realism in Image Generation

arXiv:2610.15164v1 Announce Type: new Abstract: This paper studies the backbone used by metrics for evaluating the quality of generated images. Although generative models have advanced rapidly, some o

safetyarxiv-cs-cv

TropNNC: Structured Neural Network Compression Using Tropical Geometry

arXiv:2409.03945v4 Announce Type: replace Abstract: We present TropNNC, a framework for compressing neural networks with linear and convolutional layers and ReLU-type activations using tropical geomet

Truncation Samplers Are Nested: Comparing Decoding Rules Is a Measurement Problem

arXiv:2610.15570v1 Announce Type: cross Abstract: Settling one comparison between two truncation samplers has cost six thousand A100-hours, and nothing says in advance how far apart two samplers can b

Trust Before Autonomy: Rethinking Agentic Remote Sensing for Crisis Response

arXiv:2610.18789v1 Announce Type: new Abstract: Agentic remote sensing is evaluated on how many questions it can answer, whereas crisis response depends on whether its answers can be relied on and how

Trust-Calibrated VLA Policy Post-Training with Evolving Imagination

arXiv:2610.16517v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are often pretrained via imitation learning, making reinforcement learning (RL) post-training important when deplo

TSRL: Trigger-based Supervised Reinforcement Learning for Continual Agent Training

arXiv:2610.17726v1 Announce Type: new Abstract: Large language model (LLM) agents excel at complex, multi-step tasks but suffer from catastrophic forgetting during continual learning--acquiring new sk

TStruct: Learning Shared Temporal Structures for Long-Term Time-Series Forecasting

arXiv:2610.17864v1 Announce Type: new Abstract: Long-term time series forecasting requires temporal dependency modeling that remains stable and generalizable under temporal distribution shifts. Howeve

tutorialsarxiv-cs-lg

TTT-Parkour: Rapid Test-Time Training for Perceptive Robot Parkour

arXiv:2602.02331v2 Announce Type: replace Abstract: Achieving highly dynamic parkour with humanoid robots on complex terrains unseen during the pre-training stage remains a challenge in robotics. Alth

safetyarxiv-cs-ro

Agents

Tube-Based Robust Control Strategy for Vision-Guided Autonomous Vehicles

arXiv:2503.18752v3 Announce Type: replace-cross Abstract: A robust control strategy for autonomous vehicles can improve system stability, enhance riding comfort, and prevent driving accidents. This pa

agentsarxiv-cs-cv

TurnCost: Turns, Not Observations: Cache-Aware Loop Engineering for Coding Agents

arXiv:2610.15093v1 Announce Type: cross Abstract: AI coding agents operate in iterative loops: they modify code, run tools, inspect the results, and decide what to do next. We call the design of this

TwinPrune: Density-Aware Two-Phase Token Pruning for Vision-Language Models

arXiv:2610.17530v1 Announce Type: new Abstract: We propose a unified spatial-density signal to score token importance for both inference phases: bipartite merge for prefill and KV eviction for decode,

Local Ai

Two Clocks: Why Larger Rollout Batches Learn Slower but Fail on Schedule in Reinforcement Learning with Verifiable Rewards

arXiv:2610.18853v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) relies on costly model-generated responses, motivating larger rollout batches and square-root lear

local-aiarxiv-cs-lg

Two For The Price Of One: Curvature-Aware Pruning Preserves Influence Functions Too

arXiv:2610.18516v1 Announce Type: new Abstract: Post-training pruning enables efficient deployment, but conventional methods do not explicitly preserve internal-gradient alignment between sparse and d

Two Linear Programs are Better than One

arXiv:2610.15909v1 Announce Type: cross Abstract: The extension complexity operatorname{xc}(P) of a polytope P is the minimum number of facets of a polytope that projects onto it. Motivated by the com

Two-Stage Multi-modal Learning for Dynamic Audio-Visual Navigation: From Physical Alignment to Scene Aggregation

arXiv:2610.14077v1 Announce Type: cross Abstract: Embodied Audio-Visual Navigation (AVN) requires an agent to locate sound targets by jointly leveraging visual and auditory cues. While prior work has

safetyarxiv-cs-ro

UEPS: Robust and Efficient MRI Reconstruction

arXiv:2603.18572v2 Announce Type: replace-cross Abstract: Deep unrolled models (DUM) have become the state-of-the-art for accelerated MRI reconstruction, yet their robustness under domain shift remain

ULPack: Reference-Conditioned Lossless Compression of Checkpoint Trajectories

arXiv:2610.14184v1 Announce Type: new Abstract: Studying model training requires checkpoint trajectories, whose storage cost limits temporal resolution. Although adjacent checkpoints are numerically c

UMAS3D: Unified Mask-Free 3D Editing with Region-Adaptive Source Preservation

arXiv:2610.15533v1 Announce Type: new Abstract: 3D editing aims to modify an existing 3D asset according to user intent while preserving geometry and appearance outside the intended edit region. Exist

UMM-RM: An Upcycle-and-Merge MoE Reward Model for Mitigating Reward Hacking

arXiv:2512.00724v3 Announce Type: replace Abstract: Reward models (RMs) are critical to reinforcement learning from human feedback (RLHF). However, policy models can exploit biases or spurious correla

Unbalanced W-Flow: Unbalanced Wasserstein Drifting under Finite-Batch Marginal Fluctuations

arXiv:2610.18552v1 Announce Type: cross Abstract: Teacher-free one-step generative models such as W-Flow learn directly from distributional objectives, with the Sinkhorn divergence providing the stron

tutorialsarxiv-cs-cv

Uncertainty-Aware Cortical Surface Reconstruction and Spherical Registration for Heterogeneous Brain MRI

arXiv:2610.16595v1 Announce Type: cross Abstract: Surface-based cortical analysis is useful in many applications as it is a natural way to model the highly folded structure of the cerebral cortex, all

Uncertainty-Aware Semi-Supervised Framework with Supervised Contrastive Learning for Sugarcane Leaf Disease Detection

arXiv:2610.17996v1 Announce Type: new Abstract: Sugarcane leaf diseases severely impact crop yield and quality, making accurate and timely detection critical for sustainable agriculture. Conventional

safetyarxiv-cs-cv

Uncertainty Evaluation: Bridging Machine Learning and Measurement Science

arXiv:2610.18066v1 Announce Type: new Abstract: Reliable uncertainty evaluation is essential for assessing the trustworthiness of machine learning (ML) predictions, particularly in safety-critical app

Uncovering the Generative Landscape of Diffusion Models: A Spectral-Guided Quality-Diversity Diffusion Framework

arXiv:2610.15724v1 Announce Type: new Abstract: Denoising Diffusion Probabilistic Models have emerged as powerful generative priors across domains ranging from molecular design to high-resolution imag

Understanding and Mitigating Training Instability in Large-Scale Recommender Systems: A Cascade Perspective

arXiv:2610.14394v1 Announce Type: cross Abstract: Training large-scale recommender systems on dynamic industrial platforms, such as Short-Form Video (SFV) services with billions of daily interactions,

Understanding and Reducing Misalignment in Persuasion Optimization

arXiv:2610.16070v1 Announce Type: cross Abstract: Frontier models are now trained for complex multi-agent tasks, from running a company to engaging in negotiations. These tasks necessitate training mo

Understanding Circulant Permutation to Extend the CMinHash Estimator

arXiv:2610.18551v1 Announce Type: cross Abstract: CMinHash promises a new direction for designing MinHash algorithms with reduced variance and storage space via circulant permutations. However, the or

tutorialsarxiv-cs-lg

Understanding Gene Expression-to-Image Learning: Spatial Structure and Information Fidelity

arXiv:2610.17699v1 Announce Type: cross Abstract: Single-cell RNA sequencing (scRNA-seq) enables cell-level analysis of biological heterogeneity, but learning effective representations from high-dimen

Understanding Helpfulness and Harmless Tension in Reward Models

arXiv:2606.13209v2 Announce Type: replace Abstract: Reward models (RMs) are a key component of reinforcement learning from human feedback, enabling the alignment of large language models with human va

Understanding Hidden Spatial Preferences in Bayesian Optimization

arXiv:2610.14696v1 Announce Type: new Abstract: Bayesian optimization uses posterior uncertainty to decide where to explore, but this uncertainty is not spatially neutral. On bounded domains, Gaussian

Understanding Personalization Gains in Whisper

arXiv:2610.17019v1 Announce Type: cross Abstract: Whisper-style ASR models provide strong zero-shot transcription, but their residual errors are not evenly distributed across datasets, recording condi

Understanding the Interplay between Memorization and Learning in Large Language Models

arXiv:2507.14777v2 Announce Type: replace Abstract: We investigate foundational questions about the interplay between memorization and learning in large language models (LLMs). When an LLM generates a

Unfair and Unsafe: Everyday Bias Triggers Emergent Misalignment

arXiv:2610.15961v1 Announce Type: cross Abstract: Emergent misalignment (EM) occurs when fine-tuning a language model to a narrow task makes it misaligned on unrelated ones. Because organizations now

Uni-RS: A Spatially Faithful Unified Understanding and Generation Model for Remote Sensing

arXiv:2601.17673v2 Announce Type: replace Abstract: Unified remote sensing multimodal models exhibit a pronounced spatial reversal curse: Although they can accurately recognize and describe object loc

safetyarxiv-cs-cv

UniDeblur: Unified Motion Deblurring via Fourier Convolution Learning

arXiv:2610.12941v2 Announce Type: replace Abstract: Image deblurring is a fundamental task in computer vision, yet existing methods mainly specialize in isolated scenarios. Consequently, the deblurrin

UniForge3D: Joint 3D Reconstruction and Generation in a Shared Anchor-Guided Space

arXiv:2610.15357v1 Announce Type: new Abstract: Generating complete 3D assets from sparse, unposed images requires preserving observed geometry while completing unseen regions. Joint reconstruction an

tutorialsarxiv-cs-cv

UniLAP: A Unified Latent Action Policy for Cross-Embodiment Manipulation

arXiv:2610.16969v1 Announce Type: cross Abstract: Latent Action Models (LAMs) can capture manipulation priors from action-free videos, but discrete latents usually impose a granularity bottleneck, lim