Search: “inference”

arXiv:2606.14048v2 Announce Type: replace Abstract: World action models (WAMs) have recently shown promise in jointly modeling future observations and executable robot actions. However, most existing

applicationsarxiv-cs-cv

7 Jul 2026

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During a Bloomberg interview, Yann LeCun (@ylecun ) explains why LLMs are limited in terms of real-world intelligence during a Bloomberg int…

DGX agent

During a Bloomberg interview, Yann LeCun (@ylecun ) explains why LLMs are limited in terms of real-world intelligence during a Bloomberg interview. 'Language is a very approximate, reduced, quantized,

model-releasesyann-lecun--x

6 Jul 2026

b9871

local-aillama-cpp-releases

The search results don't contain specific details about release b9871. Based on the pattern evident in the search results and the context, here's the summary: B9871 is a release of llama.cpp, an open-

4 Jul 2026

The July 4th weekend All-In @theallinpod turned into a long argument about who owns the intelligence layer. The besties think enterprises ju…

model-releasesclem-delangue--x

The July 4th weekend All-In @theallinpod turned into a long argument about who owns the intelligence layer. The besties think enterprises just woke up to a trap they had been walking into, here's how

4 Jul 2026

Beyond Skepticism: Evaluating LLMs Pedagogical Intent Reasoning with the Adaptive Pedagogical Vigilance Framework

model-releasesarxiv-cs-cl

arXiv:2607.01581v1 Announce Type: new Abstract: The capacity of Large Language Models (LLMs) to reason about pedagogical intent within instructional communication remains underexplored, particularly i

Dendritic In-Context Learning in a Single-Layer Spiking Neural Network

model-releasesarxiv-cs-lg

arXiv:2607.02283v1 Announce Type: cross Abstract: In-context learning (ICL) operates via implicit gradient descent embedded in the forward pass of modern AI architectures -- Transformers, Mamba, state

Expander Sparse Autoencoders: Parameter-Efficient Dictionaries for Mechanistic Interpretability

arXiv:2607.01799v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) decompose internal activations of neural networks into sparse linear combinations of learned features by fitting an overcom

ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues

arXiv:2607.01242v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals

Overthink-Triggered Slowdown Attacks on LVLM-Based Robotic Systems

arXiv:2607.01518v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have been increasingly integrated into robotic systems. However, these models may exhibit overthinking behaviors,

safetyarxiv-cs-ro

Purified OPSD: On-Policy Self-Distillation Without Losing How to Think

arXiv:2607.02234v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) has emerged as a promising paradigm for improving LLM reasoning, where a privileged teacher with access to reference

safetyarxiv-cs-ai

QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs

arXiv:2602.10431v4 Announce Type: replace Abstract: Large language models (LLMs) demand substantial computational and memory resources, posing challenges for efficient deployment. Two complementary ap

Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning

arXiv:2607.01511v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has emerged as an effective approach for activating latent reasoning capabilities in large language models. However, mo

Scaling with Confidence: Calibrating Confidence of LLMs for Adaptive Test Time Scaling

arXiv:2607.01612v1 Announce Type: new Abstract: Training large language models (LLMs) with reinforcement learning (RL) has significantly advanced their performance on reasoning and question-answering

Self-Gating Attention for Efficient Time Series Forecasting

arXiv:2607.02344v1 Announce Type: cross Abstract: Transformer architectures have shown strong potential in time series forecasting, where multi-head self-attention is widely used to capture temporal d

Set Diffusion: Interpolating Token Orderings Between Autoregression and Diffusion for Fast and Flexible Decoding

arXiv:2607.01775v1 Announce Type: new Abstract: Discrete diffusion models have steadily improved in quality relative to autoregressive (AR) models. However, these models are normally constrained to fi

SINA: A Fully Automated Circuit Schematic Image to Netlist Generator Using Artificial Intelligence

arXiv:2607.01609v1 Announce Type: new Abstract: Recent advances in Artificial Intelligence (AI) have revolutionized Electronic Design Automation (EDA), particularly through Large Language Models (LLMs

Spanning Tree Autoregressive Visual Generation

arXiv:2511.17089v2 Announce Type: replace-cross Abstract: We present Spanning Tree Autoregressive (STAR) modeling, which can incorporate prior knowledge of images, such as center bias and locality, to

local-aiarxiv-cs-ai

YuFeng-XGuard: A Reasoning-Centric, Interpretable, and Flexible Guardrail Model for Large Language Models

arXiv:2601.15588v2 Announce Type: replace Abstract: As large language models (LLMs) are increasingly deployed in real-world applications, safety guardrails are required to go beyond coarse-grained fil

safetyarxiv-cs-cl

AGI Maze as a Benchmark Framework for World-Modeling Agents

arXiv:2607.00627v1 Announce Type: new Abstract: Large language models (LLMs) are powerful pattern-completion systems, but their default operating mode - predicting the next token from a static context

Auditing Forgetting in Limited Memory Language Models

arXiv:2607.00605v1 Announce Type: cross Abstract: Limited Memory Language Models (LMLMs) externalize factual knowledge to a database to enable deletion-based unlearning without retraining. Existing ev

ConRTF: Edge-Constrained Boundary Distribution Refinement for Realtime TransFormer Table Structure Recognition

arXiv:2607.00734v1 Announce Type: cross Abstract: Table Structure Recognition (TSR) aims to recover the row and column layout of tables from document images, a key step in document understanding pipel

local-aiarxiv-cs-ai

Continuous Speculative Decoding for Autoregressive Image Generation

arXiv:2411.11925v3 Announce Type: replace Abstract: Continuous visual autoregressive (AR) models have demonstrated promising performance in image generation, but their inherently sequential nature res

safetyarxiv-cs-cv

DeWorldSG: Depth-Aware 3D Semantic Scene Graph Generation via World-Model Priors

arXiv:2607.00889v1 Announce Type: cross Abstract: We present DeWorldSG, a novel framework that generates spatio-temporally robust 3D Semantic Scene Graphs from RGB-D sequences. Existing methods often

Diffusion-GR2: Diffusion Generative Reasoning Re-ranker

arXiv:2607.01170v1 Announce Type: cross Abstract: Generative reasoning re-rankers achieve strong recommendation accuracy by emitting a chain-of-thought before re-ordering a candidate list, but they ar

safetyarxiv-cs-ai

DriveVer: Lightweight Trajectory Evaluator as Test-Time Verifier for Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2607.00399v1 Announce Type: new Abstract: End-to-end autonomous driving models often encounter performance bottlenecks, as training-time scaling leads to high computational costs and diminishing

FSA: An Alternative Efficient Implementation of Native Sparse Attention Kernel

arXiv:2508.18224v3 Announce Type: replace-cross Abstract: Recent advances in sparse attention mechanisms have demonstrated strong potential for reducing the computational cost of long-context training

Magic-MM-Embedding: Towards Visual-Token-Efficient Universal Multimodal Embedding with MLLMs

arXiv:2602.05275v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have shown immense promise in universal multimodal retrieval, which aims to find relevant items of various

researcharxiv-cs-cv

OpenReward: Learning to Reward Long-form Agentic Tasks via Reinforcement Learning

arXiv:2510.24636v3 Announce Type: replace Abstract: Reward models (RMs) have become essential for aligning large language models (LLMs), serving as scalable proxies for human evaluation in both traini

safetyarxiv-cs-cl

Persona Without Substrate: Regime-Dependence and the LLM Individuation Problem

arXiv:2607.00006v1 Announce Type: cross Abstract: Beckmann & Butlin's (2026) ontological framework for the LLM individuation problem inherits an unargued cross-regime co-reference assumption from the

Hardware

ROSA: A Robotics Foundation Model Serving System for Robot Factories

arXiv:2607.01088v1 Announce Type: new Abstract: Robotics foundation models (RFMs) are making general-purpose robots increasingly practical for factory deployments. While RFM serving systems are centra

hardwarearxiv-cs-ro

Training-Free Debiasing of Diffusion Models via CLIP-Guided Denoising Optimization

arXiv:2607.00817v1 Announce Type: new Abstract: Text-to-image diffusion models achieve impressive visual quality, yet demographic bias remains a challenge, as neutral prompts consistently produce ster

safetyarxiv-cs-cv

Valdi: Value Diffusion World Models

arXiv:2607.00917v1 Announce Type: cross Abstract: World models can enable Model Predictive Control (MPC), but this requires dynamics prediction that is both fast enough for online use and expressive e

A Three-Phase Foundation Model for Tax-Aware Personalized Portfolio Management

arXiv:2606.30997v1 Announce Type: new Abstract: We present a three-phase deep reinforcement learning system for personalized portfolio management that addresses three limitations shared by all prior f

Amplifying Membership Signal Through Chained Regeneration

arXiv:2606.31991v1 Announce Type: cross Abstract: The tendency of large generative models to memorize training data makes sample verification critical for privacy auditing and copyright enforcement. C

AugSplat: Radiance Field-Informed Gaussian Splatting for Sparse-View Settings

arXiv:2606.31556v1 Announce Type: new Abstract: Generating high-quality novel views at real-time frame rates remains a central challenge in 3D vision, particularly in sparse-view scenarios. Neural rad

researcharxiv-cs-cv

b9857

local-aillama-cpp-releases

Based on the available search results, the specific release details for b9857 are not fully accessible, but this entry refers to a build release from the llama.cpp project. llama.cpp is a port of Face

BEST-RQ-2: Contextualize-Then-Predict, a Two-Step Approach for Self-Supervised Audio Representations

arXiv:2606.30700v1 Announce Type: cross Abstract: Self-supervised learning enables audio representations that transfer across domains and tasks. We present BEST-RQ-2, an evolution of BEST-RQ that reta

Beyond Static Prompts: Building Scale-Proof, Polymorphic Multi-Agent Systems with Google's ADK

model-releasesgoogle-cloud-ai

As enterprise generative AI transitions from simple, conversational chatbots to autonomous multi-agent workflows, developers face a critical bottleneck: scale. In a production environment, an enterpri

Cross-Domain Feature Expansion for Tabular Medical Data via Knowledge Graphs Injection

arXiv:2606.31171v1 Announce Type: new Abstract: Acquiring comprehensive cross-domain biomedical profiles is often costly and time-consuming, resulting in severe data scarcity in medical research. To a

FeRA: Frequency-Energy Constrained Routing for Effective Diffusion Adaptation Fine-Tuning

model-releasesarxiv-cs-cv

arXiv:2511.17979v2 Announce Type: replace Abstract: Diffusion models have achieved remarkable success in generative modeling, yet how to effectively adapt large pretrained models to new tasks remains

Moral Safety in LLMs: Exposing Performative Compliance with Puzzled Cues

model-releasesarxiv-cs-cl

arXiv:2606.31644v1 Announce Type: new Abstract: As large language models take on morally consequential roles in healthcare, legal, and hiring contexts, we need to examine whether their ethical behavio

Off the Rails: Hijacking the Scoring Head in Generative End-to-End Driving Planners with Safety-Violating Adversarial Perturbations

arXiv:2606.30807v1 Announce Type: cross Abstract: Generative models have recently seen rapid adoption in End-to-End (E2E) autonomous driving (AD), with diffusion-based denoising and vocabulary-based r

safetyarxiv-cs-cv

Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models

arXiv:2606.31334v1 Announce Type: new Abstract: Joint OFDM-RIS optimization for 6G is a mixed-integer nonlinear programming (MINLP) problem covering sum-rate maximization, energy efficiency, max-min f

Physics-Constrained Fine-Tuning of Flow-Matching Models for Generation and Inverse Problems

arXiv:2508.09156v3 Announce Type: replace-cross Abstract: We present a framework for fine-tuning flow-matching generative models to enforce physical constraints and solve inverse problems in scientifi

Quantum Bayesian Networks Can Speed up Reinforcement Learning in Partially Observable Environments

arXiv:2507.18606v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) provides a principled framework for decision-making in partially observable environments, which can be modeled as

Semantic Leakage and Privacy Preservation in Relay-Assisted Semantic Communications

arXiv:2606.31973v1 Announce Type: cross Abstract: Semantic communication (SemCom) has emerged as a promising paradigm in which the transmission of task-relevant information is prioritized over raw dat

Sequential sparse Gaussian process quantile regression

model-releasesarxiv-cs-lg

arXiv:2606.31284v1 Announce Type: new Abstract: Quantile regression aims to estimate the conditional quantiles of a response variable from observed data. In a Bayesian setting, Gaussian process quanti

SpheRoPE: Zero-Shot Optimization-Free 360 Panorama Generation with Spherical RoPE

arXiv:2606.32033v1 Announce Type: new Abstract: We present a zero-shot, training-free and optimization-free framework for generating 360 panoramic images and videos by directly injecting spherical pri

researcharxiv-cs-cv