Search: “inference”

HMPO: Hybrid Median-length Policy Optimization for Chain-of-Thought Compression

arXiv:2606.01934v1 Announce Type: cross Abstract: Large language models achieve remarkable performance via extended chain-of-thought (CoT) reasoning, yet this lengthy process incurs substantial infere

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LERD: Latent Event-Relational Dynamics for Neurodegenerative Classification

DGX agent

arXiv:2602.18195v2 Announce Type: replace-cross Abstract: Alzheimer's disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics, making accurate and clinically useful EEG-base

tutorialsarxiv-cs-ai

2 Jun 2026

LK Losses: Direct Acceptance Rate Optimization for Speculative Decoding

arXiv:2602.23881v2 Announce Type: replace-cross Abstract: Speculative decoding accelerates autoregressive large language model (LLM) inference by using a lightweight draft model to propose candidate t

Applications

LongAttnComp: Cross-Family Context Compression for Long-Context Reasoning

arXiv:2606.01336v1 Announce Type: new Abstract: As real-world applications increasingly require processing inputs of 100k+ tokens, the gap between context length and inference efficiency has become a

applicationsarxiv-cs-cl

Memory-Efficient LLM Training with Dynamic Sparsity: From Stability to Practical Scaling

arXiv:2606.00888v1 Announce Type: cross Abstract: Dynamic Sparse Training (DST) offers a promising paradigm for improving the training and inference efficiency of deep neural networks; however, we fin

Motion-aware Event Suppression for Event Cameras

model-releasesarxiv-cs-cv

arXiv:2602.23204v3 Announce Type: replace Abstract: In this work, we introduce the first framework for Motion-aware Event Suppression, which learns to filter events triggered by IMOs and ego-motion in

Hardware

Observation, Not Prediction: Conversation-Level Disaggregated Scheduling for Agentic Serving

arXiv:2606.01839v1 Announce Type: cross Abstract: LLM-based agents resolve a user task through many turns of dependent inference and tool calls, producing a workload whose total cost is unknown when t

hardwarearxiv-cs-lg

Agents

OctoT2I: A Self-Evolving Agentic Text-to-Image Router

arXiv:2606.01803v1 Announce Type: new Abstract: The explosive growth of Text-to-Image (T2I) models, from large-scale versions to lightweight, real-time ones, now faces diminishing marginal returns fro

agentsarxiv-cs-ai

Off-the-Shelf LLMs as Process Scorers: Training-Free Alternative to PRMs for Mathematical Reasoning

model-releasesarxiv-cs-ai

arXiv:2606.01682v1 Announce Type: cross Abstract: Selecting the best response from multiple small-model samples using a stronger scorer is a simple inference-time strategy, but fails when the small mo

On the Collapse of Generative Paths: A Criterion and Correction for Diffusion Steering

arXiv:2512.10339v2 Announce Type: replace Abstract: Inference-time steering adapts pretrained diffusion and flow models to new tasks without retraining, often utilizing ratio-of-densities construction

On the evolution of the concept of probability as a mirror of the evolution of reason

arXiv:2606.00102v1 Announce Type: new Abstract: Over the centuries, probability theory has grown from the calculus of games of chance into a central framework for reasoning under uncertainty. This art

Private and Stable Test-Time Adaptation with Differential Privacy

arXiv:2606.01908v1 Announce Type: cross Abstract: Test-time adaptation (TTA) can reduce error on new and different data by updating the model on these inputs during inference. However, these updates r

researcharxiv-cs-cv

SENSE: Semantic Embedding Navigation with Soft-gated Evaluation for Retrieval-based Speculative Decoding

model-releasesarxiv-cs-ai

arXiv:2606.00021v1 Announce Type: cross Abstract: Speculative Decoding (SD) accelerates Large Language Model (LLM) inference by employing a lightweight draft model to propose candidate tokens, which a

SimSD: Simple Speculative Decoding in Diffusion Language Models

arXiv:2606.02544v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs, offering faster inference throug

Simultaneous Multi-objective Alignment Across Verifiable and Non-verifiable Rewards

arXiv:2510.01167v2 Announce Type: replace-cross Abstract: Aligning large language models to human preferences is inherently multidimensional, yet most pipelines collapse heterogeneous signals into a s

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SIRI: Self-Internalizing Reinforcement Learning with Intrinsic Skills for LLM Agent Training

arXiv:2606.02355v1 Announce Type: new Abstract: Long-horizon LLM agents can benefit from reusable skills, yet existing skill-based methods often rely on external skill generators during training or pe

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STARFISH: faST Accuracy Recovery in pruned networks From Internal State Healing

arXiv:2606.01126v1 Announce Type: cross Abstract: Pruning is a process designed to reduce the number of weights in a large neural network. This can substantially speed up inference but might cause a c

StreamingVLM: Real-Time Understanding for Infinite Video Streams

model-releasesarxiv-cs-ai

arXiv:2510.09608v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) could power real-time assistants and autonomous agents, but they face a critical challenge: understanding near-i

Strong Stochastic Flow Maps

arXiv:2606.01086v1 Announce Type: cross Abstract: Flow and diffusion models generate high-quality samples in many modalities; however, many network evaluations are required during inference due to num

tutorialsarxiv-cs-ai

ThinkSwitch: Context Distillation with LoRA and Weight Interpolation for Specific-Purpose Reasoning Tasks

arXiv:2606.01080v1 Announce Type: cross Abstract: Large language models often improve on difficult tasks by spending inference-time compute on a reasoning trace before producing the final answer. That

Training-Free Imitation Learning with Closed-Form Diffusion Policies

arXiv:2606.01238v1 Announce Type: cross Abstract: While diffusion-based policies have impressive performance and expressivity, their long offline training slows down the data collection and policy dep

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Training Prompt Matters: State-Adaptive Optimization for Robust Fine-Tuning

arXiv:2606.01967v1 Announce Type: new Abstract: While prompt engineering is instrumental in maximizing the capabilities of Large Language Models (LLMs) during inference, the role of prompts during tra

Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention

model-releasesarxiv-cs-cl

arXiv:2606.01243v1 Announce Type: new Abstract: Latent reasoning enables Large Language Models (LLMs) to perform multi-step inference within continuous hidden states, offering efficiency gains over ex

Hardware

ViBE: Co-Optimizing Workload Skew and Hardware Variability for MoE Serving

arXiv:2606.00735v1 Announce Type: cross Abstract: In distributed Mixture-of-Experts (MoE) inference, input-dependent token routing interacts with GPU performance variability to create persistent strag

hardwarearxiv-cs-lg

Video Reasoning without Training

arXiv:2510.17045v2 Announce Type: replace-cross Abstract: Video reasoning using Large Multimodal Models (LMMs) relies on costly reinforcement learning (RL) and verbose chain-of-thought, resulting in s

tutorialsarxiv-cs-ai

Active Timepoint Selection for Learning Measure-Valued Trajectories

arXiv:2605.30625v1 Announce Type: cross Abstract: Inferring continuous probability paths from sparse snapshots is a fundamental challenge in domains like single-cell biology, where high-fidelity data

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b9451

b9451 is the latest release of llama.cpp, published on June 1, 2026 . Llama.cpp is an LLM inference project implemented in C/C++ that enables efficient local execution of large language models. This r

b9452

llama.cpp b9452 is a release of the LLM inference C/C++ project from the ggml-org repository. This release likely includes updates to the software's core functionality, bug fixes, or new features that

b9455

Release b9455 is a build of llama.cpp, a C/C++ implementation for LLM inference . This release represents a specific commit or version update to the llama.cpp project, which is used for running large

b9464

Release b9464 is a version of llama.cpp, a project that enables LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware. This specific release likely contains upd

Consolidating Rewarded Perturbations for LLM Post-Training

arXiv:2605.31494v1 Announce Type: new Abstract: Post-training of language models is commonly framed as a sample-score-update loop implemented by gradient descent. A recent line of work, exemplified by

Early Prediction of Future Behavioral Strategy from Process Traces

arXiv:2605.30550v1 Announce Type: new Abstract: Adaptive systems often need to make task-specific decisions about people from limited evidence: a tutor may need to anticipate how a learner will approa

researcharxiv-cs-lg

Efficient Diffusion LLMs via Temporal-Spatial Parallel Decoding and Confidence Extrapolation

arXiv:2605.30753v1 Announce Type: new Abstract: Diffusion-based large language models (dLLMs) support parallel text generation via iterative denoising, yet inference remains latency-heavy because many

Graph Energy Matching: Transport-Aligned Energy-Based Modeling for Graph Generation

arXiv:2603.23398v2 Announce Type: replace-cross Abstract: Generative modeling of discrete data, such as graphs, underpins many scientific and industrial applications, including molecular discovery and

local-aiarxiv-cs-ai

How Trustpilot built a real-time architecture for data enrichment using Gemma

model-releasesgoogle-cloud-ai

Processing millions of user reviews in real-time, under strict latency and cost constraints, is no easy task. Trustpilot has been doing exactly that with custom machine learning since long before larg

HypoSpace: A Diagnostic Benchmark for Set-Valued Hypothesis Generation under Underdetermination and Sublinear Coverage Bounds

model-releasesarxiv-cs-cl

arXiv:2510.15614v3 Announce Type: replace Abstract: Many scientific problems are underdetermined: multiple distinct hypotheses are equally consistent with the same observations. In such settings, effe

IAPO: Information-Aware Policy Optimization for Token-Efficient Reasoning

arXiv:2602.19049v2 Announce Type: replace Abstract: Large language models increasingly rely on long chains of thought to improve accuracy, yet such gains come with substantial inference-time costs. We

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idSCD: Identifying Training Datasets through Semantic Correlation Descriptors

arXiv:2605.30462v1 Announce Type: cross Abstract: Can a dataset be recognized from the spurious correlations it induces during training? We argue that datasets leave dataset-specific traces in a model

LVSA: Training-Free Sparse Attention for Long Video Diffusion

arXiv:2605.31057v1 Announce Type: new Abstract: Dense self-attention is the compute and quality bottleneck of long-video diffusion inference: cost grows quadratically with the sequence length, and bey

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Tools

Make sure to join our live Spaces chat on MiniMax M3 starting in 4 hours. You can pre-submit questions by replying to this tweet.

Make sure to join our live Spaces chat on MiniMax M3 starting in 4 hours. You can pre-submit questions by replying to this tweet. MiniMax M3 is live and Together AI is powering its inference 🚀 Tomorro

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Applications

OrcaRouter: A Production-Oriented LLM Router with Hybrid Offline-Online Learning

arXiv:2605.30736v1 Announce Type: cross Abstract: The rapid development of large language models, each with distinct capabilities and inference costs, raises a practical deployment question: given an

applicationsarxiv-cs-ai

Hardware

ParisKV: Fast and Drift-Robust KV-Cache Retrieval for Long-Context LLMs

arXiv:2602.07721v3 Announce Type: replace-cross Abstract: KV-cache retrieval is essential for long-context LLM inference, yet existing methods struggle with distribution drift and high latency at scal

hardwarearxiv-cs-cl

Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics

arXiv:2605.30503v1 Announce Type: new Abstract: Learning to reach arbitrary goals from sparse feedback requires agents to infer a rich notion of reachability across state--goal pairs. Goal-conditioned

researcharxiv-cs-ro

Robust Dreamer: Deviation-Aware Latent Gaussian Memory for Action-Controlled AR Video Generation

arXiv:2605.30855v1 Announce Type: new Abstract: Frame-wise action-controlled image-to-video generation is a promising paradigm for interactive world simulation, where each control signal should elicit

tutorialsarxiv-cs-cv

Speculative Decoding Across Languages

arXiv:2605.30580v1 Announce Type: new Abstract: Speculative decoding has become a crucial component of large language model (LLM) inference, enabling faster generation by drafting multiple tokens and

Speculative Pipeline Decoding: Higher-Accruacy and Zero-Bubble Speculation via Pipeline Parallelism

arXiv:2605.30852v1 Announce Type: new Abstract: Speculative Decoding (SD) accelerates low-concurrency LLM inference by employing a draft-then-verify paradigm. However, mainstream methods typically rel

Applications

VisionPulse: Dynamic Visual Sparsity for Efficient Multimodal Reasoning

arXiv:2605.31457v1 Announce Type: new Abstract: With the rapid advancement of large multimodal models (LMMs), inference-time overhead has become a key bottleneck for real-world deployment. Existing me

applicationsarxiv-cs-cv

VolFill: Single-View Amodal 3D Scene Reconstruction with Volumetric Flow Matching

arXiv:2605.31466v1 Announce Type: new Abstract: Reconstructing the complete geometry of a scene from a single RGB image remains challenging - especially when inferring hidden structures where visual e

researcharxiv-cs-cv