Search: “inference”

Kara: Efficient Reasoning LLM Serving via Sliding-Window KV Cache Compression

arXiv:2607.01237v1 Announce Type: cross Abstract: Reasoning language models often generate long chain-of-thought (CoT), which accumulates a massive KV cache during the decoding phase and incurs high d

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kNNGuard: Turning LLM Hidden Activations into a Training-Free Configurable Guardrail

DGX agent

arXiv:2607.02072v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in domains requiring guardrails to detect unsafe, off-topic, or adversarial prompts. Existing g

safetyarxiv-cs-ai

3 Jul 2026

Latent Policy Barrier: Learning Robust Visuomotor Policies by Staying In-Distribution

arXiv:2508.05941v2 Announce Type: replace Abstract: Visuomotor policies trained via behavior cloning are vulnerable to covariate shift, where small deviations from expert trajectories can compound int

Learning Semantic Atomic Skills for Multi-Task Robotic Manipulation

arXiv:2512.18368v2 Announce Type: replace Abstract: Scaling imitation learning to diverse multi-task robot manipulation remains challenging due to suboptimal demonstrations, behavioral multi-modality,

Morphology-Aware Sample Assignment: Overcoming IoU Insensitivity for Surface Defect Detection

arXiv:2606.13723v2 Announce Type: replace-cross Abstract: Intersection-over-Union (IoU), as a pivotal metric for evaluating the spatial alignment between candidate proposals and ground-truth annotatio

MultAttnAttrib: Training-Free Multimodal Attribution in Long Document Question Answering

arXiv:2607.01420v1 Announce Type: cross Abstract: As grounded QA systems are increasingly deployed in AI assistants, accurately attributing generated answers to evidence is critical for user trust and

Object Aligner: A Configurable JSON Schema Similarity Score for Graphs, Applied to LLM Prompt Optimization

arXiv:2607.01972v1 Announce Type: cross Abstract: Large language models (LLMs) are often asked to produce JSON conforming to a fixed schema, powering information extraction, tool calling, agentic plan

Optimizing Visual Generative Models via Distribution-wise Rewards

arXiv:2607.02291v1 Announce Type: new Abstract: Conventional reinforcement learning strategies for visual generation typically employ sample-wise reward functions, yet this practice frequently results

safetyarxiv-cs-lg

OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers

arXiv:2607.02461v1 Announce Type: cross Abstract: Diffusion transformers (DiTs) achieve state-of-the-art image and video generation, but their multi-step sampling and growing parameter count make infe

PairCoder++: Pair Programming as a Universal Paradigm for Verified Code-Driven Multimodal and Structured-Artifact Generation

model-releasesarxiv-cs-cl

arXiv:2607.01883v1 Announce Type: new Abstract: Code is the medium through which large language models generate structured artifacts: charts, scientific figures, vector graphics, CAD models, 3D scenes

Hardware

PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation

arXiv:2607.01803v1 Announce Type: cross Abstract: Recent advances in 3D content generation from text or images have achieved impressive results, yet view inconsistency from 2D generators and the scarc

hardwarearxiv-cs-ro

Local Ai

Procedural Memory Distillation: Online Reflection for Self-Improving Language Models

arXiv:2607.01480v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR), along with recent selfdistillation variants such as SDPO, evaluates each rollout against a verifi

local-aiarxiv-cs-ai

Program-as-Weights: A Programming Paradigm for Fuzzy Functions

arXiv:2607.02512v1 Announce Type: cross Abstract: Many everyday programming tasks resist clean rule-based implementation, such as alerting on important log lines, repairing malformed JSON, or ranking

Quantifying the Uncertainty of Blindly Estimated Room Embeddings Using a Dispersion-Calibrated Score

arXiv:2607.01527v1 Announce Type: cross Abstract: Room embeddings derived from reverberant speech are often unreliable: speech content and recording degradation can alter the representation even when

safetyarxiv-cs-lg

Agents

Real-Time Visual Intelligence on Low-Cost UAVs: A Modular Approach for Tracking, Scanning, and Navigation

arXiv:2607.02298v1 Announce Type: new Abstract: Autonomous drones are rapidly transforming modern warfare and civil applications alike. This paper presents the development of an integrated intelligent

agentsarxiv-cs-ro

ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning

arXiv:2607.02509v1 Announce Type: new Abstract: Understanding and reasoning over long contexts has become a key requirement for deploying large language models (LLMs) in realistic applications. Althou

Restoring Linguistic Grounding in VLA Models via Train-Free Attention Recalibration

arXiv:2603.06001v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models enable robots to perform manipulation tasks directly from natural language instructions and are increasing

Self-Supervised Test-Time Tuning for Packet Loss Concealment

arXiv:2607.01823v1 Announce Type: cross Abstract: Packet loss concealment (PLC) reconstructs audio packets that are missing at the receiver, usually with a trained model whose parameters remain fixed

researcharxiv-cs-cl

Single-Channel EEG-Based Cognitive Load Assessment in Online Learning: A Hybrid Deep Learning Approach

arXiv:2607.01795v1 Announce Type: cross Abstract: Monitoring cognitive load during online learning could help instructors identify content that learners find difficult, but remote settings remove the

Subliminal Clocks: Latent Time Modelling in Diffusion Language Models

arXiv:2607.01774v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) have recently emerged as a promising alternative to autoregressive models. Unlike standard diffusion-based approaches,

The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing

arXiv:2607.02416v1 Announce Type: new Abstract: Natural Language Processing (NLP) has traditionally been published in its core disciplinary venues like ACL. However, advances in Large Language Models

researcharxiv-cs-cl

Transport Discrepancy as a Reliability Signal for Vision-Language-Action Models

arXiv:2512.01715v2 Announce Type: replace Abstract: Vision-language-action (VLA) models that generate continuous action chunks via flow matching lack an internal signal for judging whether a given pre

What LLM Agents Say When No One Is Watching: Social Structure and Latent Objective Emergence in Multi-Agent Debates

arXiv:2607.02507v1 Announce Type: new Abstract: LLM agents will increasingly act in socially structured settings where role, audience, and relational context can shape what is advantageous or costly t

Tools

30 billion tokens a month to 400 trillion in a year. That's @cursor_ai, @DecagonAI, @cartesia and hundreds of other teams choosing open infr…

Together AI highlights the rapid growth of AI token consumption across multiple companies and teams, noting an increase from 30 billion tokens monthly to 400 trillion annually, with platforms like Cur

toolstogether-ai--x

Hardware

Accelerating Discrete Diffusion Models with Parallel-In-Time Sampling

arXiv:2607.00773v1 Announce Type: new Abstract: Discrete diffusion models are widely used for learning and generating discrete distributions. As the generation process is inherently sequential, the ac

hardwarearxiv-cs-lg

Active Spatial Guidance: Eliminating Injected Positional Mechanisms in Vision Transformers

arXiv:2607.00580v1 Announce Type: new Abstract: Vision Transformers (ViTs) commonly rely on injected positional mechanisms to address self-attention's permutation invariance. Motivated by the spatial

AI Native Games: A Survey and Roadmap

arXiv:2607.00527v1 Announce Type: new Abstract: Generative AI now enables games to produce dialogue, quests, characters, images, and worlds at runtime. Yet generation alone does not make a game AI-nat

AutoSpeed: Annotation-Free Stage-Adaptive Motion Speed Learning for Robot Manipulation

arXiv:2607.01051v1 Announce Type: new Abstract: Different stages of manipulation tasks exhibit varying levels of difficulty, suggesting stage-dependent motion speeds and temporal prediction horizons.

Bridgewater just published numbers that should make every frontier lab nervous. The world's largest hedge fund tested Gemini, Claude, and GP…

model-releasesclem-delangue--x

Bridgewater just published numbers that should make every frontier lab nervous. The world's largest hedge fund tested Gemini, Claude, and GPT on six document filtering tasks its investors do every day

CAT: Confidence-Adaptive Thinking for Efficient Reasoning of Large Reasoning Models

arXiv:2607.00862v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) have achieved remarkable success on complex tasks by leveraging long chain-of-thought (CoT) trajectories, yet they frequ

ClinRAG-GRAPH: Clinical-prior Retrieval-Augmented Graph Model with Domain Adversarial Learning for Breast pCR Prediction

arXiv:2607.00798v1 Announce Type: new Abstract: Neoadjuvant chemotherapy (NAC) response prediction is clinically important for treatment stratification in breast cancer. However, robust pre-treatment

Tools

@DecagonAI @AshwinSreenivas Under the hood: 6x cost reduction per turn, p95 latency under 400ms, and models shipping weekly. https://www.tog…

Together AI announced significant performance improvements in their AI infrastructure, achieving a 6x cost reduction per turn while maintaining p95 latency under 400ms, with new models being released

toolstogether-ai--x

Decompose, Compare, and Decide: Multimodal LLMs are Implicit Few-Shot Learners

arXiv:2607.00125v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable abilities when analyzing images, yet translating these capabilities to few-shot im

researcharxiv-cs-cv

Agents

Deconfounded Lifelong Learning for Autonomous Driving via Dynamic Knowledge Spaces

arXiv:2603.14354v3 Announce Type: replace-cross Abstract: End-to-End autonomous driving (E2E-AD) systems face challenges in lifelong learning, including catastrophic forgetting, difficulty in knowledg

agentsarxiv-cs-ai

Decoupled Guidance: Disentangling Subject and Context Pathways in Text-to-Image Personalization

arXiv:2607.00766v1 Announce Type: new Abstract: Text-to-image personalization aims to generate a user-provided subject in novel scenes described by text. However, most existing methods encode subject

researcharxiv-cs-cv

'Don't Say It!': Constraints, Compliance, and Communication when Language Models Play Taboo

arXiv:2607.00601v1 Announce Type: new Abstract: The game of Taboo requires describing a target word without using a set of forbidden words, so that other players can guess it. This deceptively simple

researcharxiv-cs-cl

Efficient Neural Controlled Differential Equations via Attentive Kernel Smoothing

arXiv:2602.02157v2 Announce Type: replace Abstract: Neural Controlled Differential Equations (Neural CDEs) provide a powerful continuous-time framework for sequence modeling, yet the roughness of the

researcharxiv-cs-lg

Local Ai

EFlow: Learning Evidence Flow for Long-Video Reasoning with Adaptive Reflection

arXiv:2607.00867v1 Announce Type: new Abstract: Long-video reasoning is fundamentally constrained by how models acquire and utilize visual evidence. Existing tool-augmented video frameworks often inte

local-aiarxiv-cs-cv

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients

arXiv:2607.00215v1 Announce Type: new Abstract: Neural Motion Planners (NMPs) enable fast reactive motion generation, but adapting them to new environments typically requires recollecting large expert

End-to-End Training for Autoregressive Video Diffusion via Self-Resampling

model-releasesarxiv-cs-cv

arXiv:2512.15702v2 Announce Type: replace Abstract: Autoregressive video diffusion models hold promise for world simulation but are vulnerable to exposure bias arising from the train-test mismatch. Wh

Energy-Efficient Real-Time 4-Stage Sleep Classification at 10-Second Resolution

arXiv:2508.11664v2 Announce Type: replace-cross Abstract: Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia. However, conventional methods like

researcharxiv-cs-lg

EPO: Boosting 3D Foundation Models with Edge-based Pose Optimization

arXiv:2607.00579v1 Announce Type: new Abstract: We introduce extbf{Edge-based Pose Optimization (EPO)}, a trackless geometric optimization framework specifically designed to boost the Structure-from-M

EquiSteer: Cross-Attention Steering Towards a Fairer Text-Guided Image Generation

arXiv:2607.01147v1 Announce Type: new Abstract: Text-to-image diffusion models power everyday creative tasks, but they still reproduce the demographic biases in their training data. On common prompts

Applications

FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model

arXiv:2607.01212v1 Announce Type: cross Abstract: Current work on robot furniture assembly mostly focuses on toy-scale settings or single-arm manipulation. We introduce FurnitureVLA, the first systema

applicationsarxiv-cs-ai

FusionFactory: Fusing LLM Capabilities with Multi-LLM Log Data

model-releasesarxiv-cs-lg

arXiv:2507.10540v3 Announce Type: replace Abstract: The rapid advancement of large language models (LLMs) has created a diverse landscape of models, each excelling at different tasks. This diversity d

GEAR-Seg: A Grounded Explainable Agent for Reasoning Segmentation and Data Engine

model-releasesarxiv-cs-cv

arXiv:2607.00544v1 Announce Type: new Abstract: Reasoning segmentation requires localizing targets based on complex, implicit queries. Current end-to-end models typically entangle perception and deduc

GRACE-RAG: Governed Retrieval Architecture for Canonical Evidence Synthesis, Enabling Lightweight Deployment in Closed-Domain Institutional Settings

arXiv:2607.00013v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are widely used in institutional question answering settings where responses must be grounded in authorit

HARC: Coupling Harmfulness and Refusal Directions for Robust Safety Alignment

arXiv:2607.00572v1 Announce Type: new Abstract: Understanding how aligned LLMs internally represent safety is critical for diagnosing alignment vulnerabilities, as it explains why jailbreaks succeed a