Search: “inference”

An Asymptotic Theory of Chain-of-Thought in In-Context Learning

model-releasesarxiv-cs-lg

arXiv:2606.03217v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning has become a widely used mechanism for eliciting multi-step reasoning in large language models by generating intermed

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Attend to Anything: Foundation Model for Unified Human Attention Modeling

DGX agent

arXiv:2606.03540v1 Announce Type: new Abstract: Existing human attention (saliency) modeling methods persist as highly fragmented across modalities, scenes, and task formulations. Consequently, even w

applicationsarxiv-cs-cv

3 Jun 2026

Agents

AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling

arXiv:2606.03664v1 Announce Type: cross Abstract: Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for app

agentsarxiv-cs-ai

AutoTail-BSFGM: Class-Balance-Aware Fine-Tuning for Chinese Scholarly Text Classification

arXiv:2606.03576v1 Announce Type: new Abstract: Scholarly text classification supports literature organization, subject indexing, and research intelligence, but Chinese scholarly corpora often contain

researcharxiv-cs-cl

b9494

local-aillama-cpp-releases

The search results do not contain specific details about the b9494 release. Based on the context from the llama.cpp GitHub releases page, b9494 is an intermediate build release of llama.cpp, a C/C++ p

b9496

local-aillama-cpp-releases

The search results do not contain specific information about release b9496. Based on the available context, b9496 is a build/release version of llama.cpp, a C/C++ implementation for efficient LLM infe

BEV-ODOM2: Enhanced BEV-based Monocular Visual Odometry with PV-BEV Fusion and Dense Flow Supervision for Ground Robots

model-releasesarxiv-cs-ro

arXiv:2509.14636v2 Announce Type: replace Abstract: Scale-consistent ego-motion estimation is fundamental for autonomous ground robots. Bird's-Eye-View (BEV) representation naturally addresses the sca

Beyond Single Solution: Multi-Hypothesis Collaborative Deep Unfolding Network for Image Compressive Sensing

arXiv:2606.03666v1 Announce Type: new Abstract: Recent deep unfolding networks (DUNs) have advanced Compressive Sensing (CS) by effectively integrating iterative optimization with deep learning archit

researcharxiv-cs-cv

Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching

arXiv:2606.03911v1 Announce Type: new Abstract: Modern generative models possess a deep understanding of visual content, yet training them for image editing typically requires massive datasets of pair

researcharxiv-cs-cv

Building Reliable Long-Form Generation via Hallucination Rejection Sampling

arXiv:2606.03628v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved remarkable progress in open-ended text generation, yet they remain prone to hallucinating incorrect or unsu

Buzz, Choose, Forget: A Meta-Bandit Framework for Bee-Like Decision Making

arXiv:2510.16462v3 Announce Type: replace Abstract: This work introduces MAYA, a sequential imitation learning model based on multi-armed bandits, designed to reproduce and predict individual bees' de

researcharxiv-cs-lg

Causal Neural Probabilistic Circuits

arXiv:2603.01372v2 Announce Type: replace-cross Abstract: Concept Bottleneck Models (CBMs) enhance the interpretability of end-to-end neural networks by introducing a layer of concepts and predicting

Causal Preference Elicitation

arXiv:2602.01483v2 Announce Type: replace-cross Abstract: We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relati

CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery

arXiv:2606.03602v1 Announce Type: cross Abstract: Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical dis

ConTraIRL: Factorized Contrastive Abstractions for Transferable IRL

arXiv:2606.03017v1 Announce Type: cross Abstract: Reward transfer in Inverse Reinforcement Learning (IRL) is unreliable when policies must generalize to unseen combinations of environment dynamics and

CP-Agent: Context-Aware Multimodal Reasoning for Cellular Morphological Profiling under Chemical Perturbations

arXiv:2606.03435v1 Announce Type: new Abstract: Cell Painting combines multiplexed fluorescent staining, high-content imaging, and quantitative analysis to generate high-dimensional phenotypic readout

Cross-Lingual Token Arbitrage: Optimizing Code Agent Context Windows via Local LLM Preprocessing

arXiv:2606.03618v1 Announce Type: new Abstract: AI-assisted coding agents are bottlenecked by input-token cost. Two pathologies of raw human input drive much of this overhead: tokenization inefficienc

Cross-Modality Feature Fusion Based on Structured State Space Duality for Multimodal Image Registration Network

arXiv:2606.03341v1 Announce Type: new Abstract: In multi-modal image registration, the primary challenge lies in shared structural information extraction. Compared to Transformers, Structured State Sp

local-aiarxiv-cs-cv

Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection

arXiv:2606.03251v1 Announce Type: new Abstract: In nature, events that affect some individuals or groups but not others constitute an implicit intervention and are known as natural experiments. For ex

applicationsarxiv-cs-ai

DTKG: Dual-Track Knowledge Graph-Verified Reasoning Framework for Multi-Hop QA

arXiv:2510.16302v2 Announce Type: replace Abstract: Multi-hop reasoning for question answering (QA) plays a critical role in retrieval-augmented generation (RAG) for modern large language models (LLMs

E2LLM: Towards Efficient LLM Serving in Heterogeneous Edge/Fog Environments

arXiv:2606.03770v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become integral to modern applications, yet their deployment remains challenging. Beyond executing the models themse

applicationsarxiv-cs-ai

Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis

arXiv:2606.03812v1 Announce Type: new Abstract: Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems, demand reliable hazard identifica

Fast Organic Crystal Structure Prediction with Unit Cell Flow Matching

arXiv:2606.03199v1 Announce Type: new Abstract: Organic crystal structure prediction (CSP) is a requirement for computational modelling of organic solids, but traditionally costs several CPU-years per

safetyarxiv-cs-lg

From Local Training to Large-Scale Mapping: A Comparative Assessment of Machine Learning and Deep Learning for Transferable Satellite-Derived Bathymetry

arXiv:2606.02764v1 Announce Type: new Abstract: Satellite-derived bathymetry (SDB) from multispectral imagery is cost-effective but scales poorly across regions, especially in optically complex coasta

From Long News to Accurate Forecast: Importance-Aware Fusion and PRM-Guided Reflection for Time Series Forecasting

arXiv:2606.03097v1 Announce Type: new Abstract: Incorporating news into time series forecasting is appealing because news can reveal abrupt exogenous events that historical values alone cannot recover

FSA-GRPO: Teaching Auditory LLMs to Use Few-shot Demonstrations

arXiv:2606.02615v1 Announce Type: cross Abstract: Few-shot prompting provides an effective way to adapt auditory large language models to low-resource tasks such as children's speech recognition. Howe

GeoDrive-Bench: Benchmarking Region-Specific Multimodal Reasoning in Autonomous Driving

arXiv:2606.02774v1 Announce Type: new Abstract: Vision-language models (VLMs) for autonomous driving have shown promising performance, but their ability to handle region-specific traffic rules remains

Glass Box at Orbit: A Constitutional AI Verification Framework for Trustworthy Autonomous CubeSat Intelligence

arXiv:2606.02967v1 Announce Type: cross Abstract: The space industry is quietly building toward something nobody has fully reckoned with: orbital data centers running thousands of autonomous AI worklo

Hardware

GRZO: Group-Relative Zeroth-Order Optimization for Large Language Model Fine-Tuning

arXiv:2606.02857v1 Announce Type: cross Abstract: Zeroth-order (ZO) optimization is a memory-efficient alternative to backpropagation for fine-tuning large language models, but its deployment is limit

hardwarearxiv-cs-ai

GuidedBridge: Training-freely Improving Bridge Models with Prior Guidance

arXiv:2606.03119v1 Announce Type: cross Abstract: Guidance methods, such as classifier-free guidance (CFG) and auto-guidance (AG), have advanced noise-to-data generation in diffusion models. Recently,

Hallucination Is Linearly Decodable from Mid-Layer Hidden States in Quantized LLMs

model-releasesarxiv-cs-cl

arXiv:2606.02628v1 Announce Type: cross Abstract: We investigate whether open-source LLMs encode a linearly separable truthfulness signal in their hidden states, and at which network depth this signal

Harvey just published a study showing a hybrid setup, open source GLM 5.1 as primary worker, routing to Opus 4.7 only when needed beats pure…

applicationsclem-delangue--x

Harvey just published a study showing a hybrid setup, open source GLM 5.1 as primary worker, routing to Opus 4.7 only when needed beats pure Opus 4.7 on quality and costs less. This is the multi-model

Hybrid Adaptive Kalman Filtering for Data-Efficient Joint Tracking and Classification

arXiv:2606.02767v1 Announce Type: cross Abstract: Kalman filtering performance is highly sensitive to model mismatch and noise covariance tuning. Learning-based approaches address these limitations bu

applicationsarxiv-cs-lg

Hybrid Autoregressive-Diffusion Model for Real-Time Sign Language Production

arXiv:2507.09105v4 Announce Type: replace Abstract: Earlier Sign Language Production (SLP) models typically relied on autoregressive decoding, which naturally preserves temporal causality but suffers

applicationsarxiv-cs-cv

Ideogram 4.0 Just Open Sourced!

local-air-stablediffusion

Ideogram released version 4.0 of its text-to-image model as an open-weight model with native 2K resolution, bounding box control, and improved text rendering. The model weights are available on GitHub

Agents

Inducing Reasoning Primitives from Agent Traces

arXiv:2606.02994v1 Announce Type: new Abstract: ReAct-style LLM agents often rediscover the same reasoning routines across problems, yet leave those routines trapped in transient scratchpads. We intro

agentsarxiv-cs-ai

It's been a while! 4 nice additions to the open-weight local-LLM-on-consumer-hardware ecosystem:

local-aisebastian-raschka--x

This post highlights four recent improvements to the ecosystem of open-weight large language models designed to run efficiently on consumer hardware, covering developments that make local LLM deployme

Large Language Models Are Overconfident in Their Own Responses

arXiv:2606.03437v1 Announce Type: new Abstract: Prior work has shown that instruction-tuned large language models (LLMs) are less well calibrated than their base pre-trained counterparts. However, lit

safetyarxiv-cs-cl

Learning Unmasking Policies for Diffusion Language Models

arXiv:2512.09106v4 Announce Type: replace Abstract: Diffusion (Large) Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks, while holding the

safetyarxiv-cs-lg

Agents

MariData: One-Step Unpaired Image Translation for Maritime Environments

arXiv:2606.03246v1 Announce Type: new Abstract: The development on robust perception systems for Maritime Autonomous Surface Ships (MASS) is heavily constrained by the scarcity of diverse training dat

agentsarxiv-cs-cv

MARIO: Motion-Augmented Real-Time Multi-Sensor Inertial Odometry

arXiv:2606.02996v1 Announce Type: cross Abstract: Inertial odometry (IO) using only Inertial Measurement Units (IMUs) provides a lightweight solution for human motion tracking in augmented reality (AR

MemoGen: Can Past Experience Improve Future Text-to-Image Generation?

arXiv:2606.03243v1 Announce Type: new Abstract: Modern text-to-image models have achieved strong visual synthesis, yet remain unreliable when prompts require implicit visual constraints, relational re

Multilingual Unlearning in LLMs: Transfer, Dynamics, and Reversibility

model-releasesarxiv-cs-cl

arXiv:2606.03291v1 Announce Type: new Abstract: Large language models (LLMs) can memorize sensitive facts, motivating unlearning methods that remove targeted knowledge without costly retraining. Howev

Multiple Choice Learning of Low-Rank Adapters for Language Modeling

arXiv:2507.10419v3 Announce Type: replace-cross Abstract: We propose LoRA-MCL, a training scheme that extends next-token prediction in language models with a method designed to decode diverse, plausib

Neural Attention Search Linear: Towards Adaptive Token-Level Hybrid Attention Models

arXiv:2602.03681v2 Announce Type: replace Abstract: The quadratic computational complexity of softmax transformers has become a bottleneck in long-context scenarios. In contrast, linear attention mode

researcharxiv-cs-cl

Outsmarting the Chameleon: Counterfactual Decoupling for Tactical OOD Shifts in Live Streaming Risk Assessment

model-releasesarxiv-cs-lg

arXiv:2606.02946v1 Announce Type: new Abstract: Live streaming has emerged as a primary medium for social interaction and digital commerce, yet it is increasingly plagued by sophisticated risks. A fun

Tutorials

Physics-informed diffusion models in spectral space

arXiv:2602.09708v2 Announce Type: replace-cross Abstract: We propose physics-informed spectral diffusion (PISD), a methodology that combines generative latent diffusion models with physics-informed ma

tutorialsarxiv-cs-ai

PixVOD: Pixel-Distributed Direct Visual Odometry and Depth Estimation

arXiv:2606.03989v1 Announce Type: new Abstract: Images composed of 2D pixel arrays are the standard input to computer vision algorithms, yet many underlying computations can be distributed across pixe

researcharxiv-cs-cv