Search: “inference”

Constitutional adapters: Inference-time interventions for misalignment and misuse

arXiv:2609.36657v1 Announce Type: cross Abstract: Training models to act in accordance with an explicitly defined set of principles, or 'constitution,' has shown promise as a robust and transparent me

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How to Run Statistics over LLM Judges and Trust the Results: Calibrated Inference for Small-Sample AI Evaluation with evalstats

DGX agent

arXiv:2609.35815v1 Announce Type: cross Abstract: Researchers across academia increasingly base significance claims on LLM judge scores and small-sample AI evaluations. Yet without well-calibrated con

safetyarxiv-cs-ai

30 Sept 2026

ImbalancE: Inference-Time Latent Search Against Degree Imbalance in Link Prediction

arXiv:2609.36996v1 Announce Type: new Abstract: Knowledge Graph Embedding models have been extensively used to learn representations of entities and relations in Knowledge Graphs for predicting missin

Latent Inference-Time Guidance of Time Series Foundation Models

arXiv:2609.38058v1 Announce Type: cross Abstract: Time Series Foundation Models (TSFMs) currently provide state-of-the-art results in forecasting tasks. They are available out-of-the-box and rely on i

Mirror-Score: Calibrated, Inference-only Scoring Exposes the Limits of Sequence-compatibility Ranking in D-peptide Design

arXiv:2609.36057v1 Announce Type: new Abstract: D-peptides combine protease resistance with high target specificity, but computational design of D-peptide binders remains immature. Mirror-Peptidizer i

SCOPE: Observation-Conditioned Full-Target Prediction for Sparse PDE Inference

arXiv:2609.36527v2 Announce Type: new Abstract: Recovering complete physical fields from sparse observations is challenging because the measurements may not uniquely determine the underlying state. Di

The Geometry of Inference in Transformer Residual Streams

arXiv:2609.37824v1 Announce Type: new Abstract: Transformer language models build predictions through successive residual updates, but how their representations become specific to an eventual outcome

safetyarxiv-cs-cl

Thinking Before Thinking: Scaling Agentic Inference Through Meta-Reasoning

arXiv:2609.38147v1 Announce Type: new Abstract: As agents take on longer and more complex problems, controlling the execution becomes a task in its own right. Each step in the run brings new control c

Tutorials

UNBIND: UNlearning By INference-time Directional Steering for Code LLMs

arXiv:2609.35913v1 Announce Type: cross Abstract: Code large language models acquire programming capabilities from large code corpora, but can also memorize implementations that later require removal.

tutorialsarxiv-cs-ai

Leaky Students: Membership Inference against On-Policy Distillation

arXiv:2609.33136v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student to match a teacher's next-token distributions on student-generated trajectories. However, privileged infor

safetyarxiv-cs-lg

MaskCoFT: Masked Co-Adaptive Fine-Tuning for Memory-Efficient MoE Inference

arXiv:2609.34077v1 Announce Type: new Abstract: Mixture-of-experts (MoE) language models often exceed the memory of a single GPU. Expert offloading keeps most experts in host memory and loads them on

MiCo: Mutual Information Coverage Optimization through Semantic Erasure Modeling for Efficient MLLM Inference

arXiv:2609.34330v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated impressive performance in multimodal understanding, but processing large numbers of visual to

researcharxiv-cs-cv

One Sequence, Many Decodings: CAGenMol-2 Recasts Drug Design as Masked Molecular Inference

arXiv:2609.34301v1 Announce Type: new Abstract: Drug design couples property evaluation, conditional generation, structure-based design, and local optimization, yet machine learning systems typically

local-aiarxiv-cs-lg

RIDE: Reference-Anchored Inference-Time Diffusion Editing for Scaffold Hopping

arXiv:2609.35623v1 Announce Type: cross Abstract: Scaffold hopping is a critical task in drug discovery, which seeks to discover new, structurally distinct molecules that share key functional groups a

Safety-Constrained Cascade Inference for Robust Malaria Cell Classification Under Field Corruptions

arXiv:2609.33005v1 Announce Type: new Abstract: Automated malaria diagnosis from thin blood-smear microscopy could meaningfully reduce the burden on under-resourced laboratories, but a model that maxi

safetyarxiv-cs-cv

Sol-H3: Recursive Self-Improvement for MiniMax-H3 Inference Acceleration on Sol-Engine across Cloud and Edge

arXiv:2609.35110v1 Announce Type: cross Abstract: Video diffusion models are rapidly scaling and exhibiting enhanced generation capabilities. Among these recent advancements, MiniMax-H3 stands out as

local-aiarxiv-cs-cv

Statistical Testing for Multiple Instance Learning via Selective Inference with Applications to Computational Pathology

arXiv:2609.31712v1 Announce Type: new Abstract: Multiple instance learning (MIL) is widely used in computational pathology because it enables weakly supervised analysis of whole-slide images (WSIs) wi

researcharxiv-cs-cv

Steer2Grasp: Inference-Time Embodiment-Aware Steering for Diverse Physically Feasible Grasp Diffusion

arXiv:2609.33546v1 Announce Type: new Abstract: Current grasp diffusion models provide rich priors for generation, yet their object-centric approach can violate the kinematic and collision constraints

local-aiarxiv-cs-ro

Adaptive multi-resolution Gaussian processes: Scalable exact inference with naturally data-sparse covariance matrices

arXiv:2609.30348v1 Announce Type: cross Abstract: Gaussian processes constitute a cornerstone of probabilistic machine learning, yet scaling them to large datasets typically forces a trade-off between

researcharxiv-cs-ai

Inference-Time Target Speaker Unlearning in LLM-Based Automatic Speech Recognition

arXiv:2609.30439v1 Announce Type: new Abstract: We introduce target-speaker unlearning ASR (TSU-ASR) task in a fully end-to-end framework for multi-speaker ASR and diarization. Given a multi-speaker u

LUCID: Learning Under Confounding for Inference and Discovery in Time Series

arXiv:2609.31315v1 Announce Type: new Abstract: Unobserved common causes are pervasive in real-world time series and can induce spurious associations that causal discovery methods mistake for direct e

Modeling Student Sensemaking with LLMs and Knowledge-Graph-Guided Inference

arXiv:2609.31046v1 Announce Type: new Abstract: Collaborative science learning requires nuanced interpretation of student dialogue to characterize how learners identify knowledge gaps, build explanati

Qwen 3.8 flash next is based on Qwen 4 architecture, if the announced Qwen 4 27b is also the same architecture with n-grams does it mean I can actually have faster inference on a single 3090 without tweaking much?

model-releasesr-localllama

I wish Qwen also released dataset and method to fully train a model ourselves but it is what it is. However, I come here with my stupid question because someone can answer it better. And will the mode

26 Sept 2026

Agents

Graph-Based Inference and Topology-Aware Multi-Agent Reinforcement Learning for Large-Scale Railway Network Management

arXiv:2609.30150v1 Announce Type: new Abstract: Modern infrastructure asset management constitutes a complex sequential decision-making problem, characterized by long planning horizons and system-leve

agentsarxiv-cs-lg

25 Sept 2026

MemGuard-Alpha: Limits of Membership Inference for Detecting and Filtering Memorization-Contaminated Signals in LLM-Based Financial Forecasting

arXiv:2603.26797v2 Announce Type: replace Abstract: Large language models are increasingly used to generate financial alpha signals, but many have memorized the historical data in their training corpo

25 Sept 2026

Selective Inference for Deep Clustering in Latent Spaces

arXiv:2609.28756v1 Announce Type: cross Abstract: Deep clustering is a powerful approach for discovering meaningful structures in high-dimensional data by learning a low-dimensional latent representat

safetyarxiv-cs-lg

25 Sept 2026

Tutorials

Active Learning for Biodiversity Monitoring: From Label Efficiency to Reliable Ecological Inference

arXiv:2609.27409v1 Announce Type: new Abstract: Limited expert annotation capacity is a pervasive constraint in biodiversity monitoring. Passive acoustic recorders and camera traps generate data faste

tutorialsarxiv-cs-lg

FLINT: Fast Lightweight Inference for Traversability

arXiv:2609.26857v1 Announce Type: cross Abstract: Navigation in off-road conditions is challenging due to the lack of structure. There is no fixed vocabulary for what is traversable. The traversabilit

HINT-Blimp: Human INTent Inference from Multimodal Cues for Robotic Blimps

arXiv:2609.27154v1 Announce Type: new Abstract: In human-robot interaction, traditional interfaces such as joysticks and handheld tablets introduce latency into navigation tasks and require the operat

researcharxiv-cs-ro

MINER: Multi-crop INference-time Enhancement for Rare-Object Retrieval with Frozen Dual Encoders

model-releasesarxiv-cs-cv

arXiv:2609.27142v1 Announce Type: new Abstract: Text-to-image retrieval with frozen dual encoders degrades when the query names a small, visually subordinate object in a cluttered scene: a single glob

Scribd, Inc. classifies more than 400 million documents with Gemini batch inference on Gemini Enterprise

model-releasesgoogle-cloud-ai

Scribd, Inc. is home to one of the world's largest collections of human-created content. Scribd’s products leverage one of the world's largest collections of human-created content and intelligent tool

Hardware

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

arXiv:2609.25624v1 Announce Type: cross Abstract: Large language model (LLM) outputs are expected to be reproducible under greedy decoding, yet in practice the same model, prompt, and software stack p

hardwarearxiv-cs-lg

Differentiable Fuzzy Inference Layer: A Monotone, Compositional Ordinal Reasoning Head for Large Language Models

arXiv:2609.26113v1 Announce Type: new Abstract: A state-of-the-art language model asked to interpret 'most of most students passed' typically answers 'most,' though composing two instances of 'most' y

Applications

Graph-Based Inference for Feedback-Driven Word Deduction: A Scalable Framework for the Jotto Problem

arXiv:2609.25056v1 Announce Type: new Abstract: A feedback-based word deduction framework based on the Jotto problem is proposed, and the problem space is represented as a weighted graph where all val

applicationsarxiv-cs-cl

Tutorials

MSA-CITE: A Co-Adapted LoRA Specialist Ecology for Fixed-Budget Small-Model Inference

arXiv:2609.26217v1 Announce Type: new Abstract: Compact language models are typically deployed by retaining a single post-training checkpoint and sampling it repeatedly. In this work, we challenge thi

tutorialsarxiv-cs-lg

Semantic Abstraction for Natural Language Inference: a Methodological Framework for Discovering and Compensating Semantic Knowledge and Reasoning Gaps in Large Language Models

arXiv:2609.26610v1 Announce Type: new Abstract: Despite their outstanding performance on many NLP tasks, LLMs face serious challenges related to semantic abstraction. In this study, we are interested

Combinatorial Inference on the Optimal Assortment in Multinomial Logit Models

arXiv:2301.12254v5 Announce Type: replace-cross Abstract: Assortment optimization has received active explorations in the past few decades due to its practical importance. Despite the extensive litera

ILRR: Inference-Time Steering Method for Masked Diffusion Language Models

arXiv:2601.21647v2 Announce Type: replace Abstract: Discrete Diffusion Language Models (DLMs) offer a promising non-autoregressive alternative for text generation, yet effective mechanisms for inferen

Memory That Looks Forward: A Zero-Inference Prospective Term for Personal Memory Retrieval

model-releasesarxiv-cs-cl

arXiv:2609.22091v1 Announce Type: new Abstract: Retrieval over a personal memory store is retrospective: it surfaces what resembles the query, and it is blind to what the user has committed to do. We

PRQuant: Permutation Residual Quantization for Low-Overhead Inference

arXiv:2609.22106v1 Announce Type: cross Abstract: Accuracy of Low-bit quantization of linear layers is often dominated by a small number of outliers. Although existing methods, such as smoothing, rota

researcharxiv-cs-cv

Triggers and Diagnostics for LLM-Based Interpretability Failures in Active Inference Agents

arXiv:2609.23215v1 Announce Type: new Abstract: LLM explainers are increasingly attached to autonomous agents as runtime oversight, with operators reading a generated account of the agent's beliefs an

Fewer Steps, Better Actions: Rethinking Flow-Matching Inference for VLA Policies

arXiv:2609.21216v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies based on flow matching generate action chunks through repeated evaluations of an action expert. Increasing the n

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Gewell - Gemma4 inference engine

model-releasesr-localllama

# the What An engine to run Gemma 4 31B on blackwell under massive concurrency and rather specific workload patterns. I've been waiting for someone to do ninfer but for gemma, and, well, ended up havi

HE-Guardrail: A Homomorphic Guardrail Against Jailbreak Attacks for Encrypted Large Language Model Inference

arXiv:2609.21484v1 Announce Type: cross Abstract: Homomorphic encryption (HE) has emerged as a promising approach to privacy-preserving machine learning (PPML), enabling computation directly over encr

Samsone: A Family of Open Small Audio Language Models for On-Device Inference

arXiv:2609.21666v1 Announce Type: cross Abstract: The success of Large Audio Language Models has driven the development of massive multimodal networks exceeding billions of parameters. However, the de

local-aiarxiv-cs-ai

SpecQuant: Speculative Decoding with Multi-Parent Quantization for Adaptive LLM Inference

arXiv:2609.21704v1 Announce Type: new Abstract: Running large language models (LLMs) locally continues to be limited by restrictions of compute and memory on consumer hardware. The popular acceleratio

local-aiarxiv-cs-lg

Elastic Spectral State Space Models for Train-Once Budgeted Inference

arXiv:2601.22488v3 Announce Type: replace Abstract: Modern sequence models are typically trained at a fixed computational capacity, while real-world applications require deployment across platforms wi

18 Sept 2026

MAGNETAR: Multipath-Guided Spatial Posteriors for Transmitter Pose Inference in the Upper Mid-Band