Search: “inference”

FaithEyes: Towards Faithful Tool Use via Multi-Agent Process-Image Verification

arXiv:2607.28225v1 Announce Type: new Abstract: Agentic vision-language models (VLMs), which interleave textual reasoning with explicit tool calls such as cropping and code-based image manipulation, h

agentsarxiv-cs-cv

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Fewer Clarifications, Better Code: Benchmarking Cross-Session Personalized Ambiguity Adaptation in Coding Assistants

DGX agent

arXiv:2607.26611v1 Announce Type: new Abstract: AI-assisted coding increasingly translates informal user intent into executable software, yet coding requests often contain ambiguities that recur in us

model-releasesarxiv-cs-ai

31 Jul 2026

FinCacheServe: Dependency-Consistent Answer Reuse for Cost-Efficient RAG Serving over Mutable Enterprise Documents

arXiv:2607.26076v1 Announce Type: cross Abstract: Retrieval-augmented generation services over mutable enterprise documents repeatedly execute semantically equivalent analysis requests. Answer reuse c

hardwarearxiv-cs-ai

Frontis-MA1: Training an AI4AI Model towards Recursive Self-Improvement in Machine Learning Engineering

model-releasesarxiv-cs-cl

arXiv:2607.28568v1 Announce Type: new Abstract: Recursive self-improvement (RSI) requires AI systems that improve the process of building AI (i.e., AI4AI); machine learning engineering (MLE) offers a

GGC: Selective Query Correction for Reliable Text-to-SPARQL Generation

arXiv:2607.28082v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities in structured query generation, making them a natural choice for Text-to-SPARQL, whic

researcharxiv-cs-cl

Good Rankers, Bad Objectives: Bilinear Contrastive Critics under Expressive Policy Search

model-releasesarxiv-cs-lg

arXiv:2607.27422v1 Announce Type: new Abstract: Good action rankings do not make a contrastive critic safe to maximize. These critics increasingly act as value-like objectives for best-of-K selection,

Graph Is the Verifier: Agentic Reinforcement Learning for Interprocedural Vulnerability Detection

arXiv:2607.26656v1 Announce Type: cross Abstract: Real-world vulnerabilities often span multiple functions, yet most learning-based detectors classify each function in isolation: on a sample of real C

safetyarxiv-cs-ai

Harnessing the Potential of Optimizing Data Mixtures via Bayesian Domain Reweighting

arXiv:2607.27928v1 Announce Type: new Abstract: The performance of Large Language Models (LLMs) is fundamentally influenced by the distributional composition of multi-domain pre-training data. While m

safetyarxiv-cs-lg

HOMER: Huber-of-Means for Efficient and Robust Estimation in Hilbert Spaces

arXiv:2607.27532v1 Announce Type: cross Abstract: Heavy tails weaken high-confidence control for the empirical mean. Geometric median-of-means (MOM) also lacks a threshold that moves toward mean effic

researcharxiv-cs-lg

Improving Item Discoverability in e-Commerce Search via Related Intent Generation

arXiv:2607.27172v1 Announce Type: cross Abstract: Traditional search systems are optimized to retrieve items that strictly match a query, often prioritizing precision over recall. In e-commerce market

researcharxiv-cs-ai

Applications

Information Bottleneck Learning for Faithful Time Series Forecasting Explanations

arXiv:2607.28124v1 Announce Type: new Abstract: As forecasts increasingly drive decisions in fields such as energy, transportation, and healthcare, understanding the historical data behind these predi

applicationsarxiv-cs-lg

Local Ai

Kohn-Sham Spectral Embedding on Sparse Graphs at the Nishimori Temperature for Image Classification

arXiv:2607.28428v1 Announce Type: cross Abstract: We introduce Kohn--Sham Spectral Embedding (KSSE), a physics-inspired energy-based model replacing dense CNN classifiers with a sparse-graph spectral

local-aiarxiv-cs-cv

Large scale cross-regional remote sensing flood monitoring framework for operative mapping and impact analysis

arXiv:2607.28401v1 Announce Type: new Abstract: Effective flood monitoring is critical for minimizing the impacts of flood disasters on populations and infrastructure. Yet reliable remote sensing acro

Tutorials

Latent Matters: Learning Deep State-Space Models

arXiv:2602.23050v2 Announce Type: replace Abstract: Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data. They are often trained by

tutorialsarxiv-cs-lg

Tutorials

Learning to Detect Cyber Attacks: Neural Anomaly Detection for Cybersecurity with Theoretical Insights

arXiv:2409.08521v2 Announce Type: replace-cross Abstract: In cybersecurity practice, new forms of cyberattacks continuously emerge, deliberately designed to evade defense systems that rely on previous

tutorialsarxiv-cs-lg

LM-GRASP: Instance-Specific Language Models for Combinatorial Construction via Online Imitation Learning

model-releasesarxiv-cs-lg

arXiv:2607.28135v1 Announce Type: new Abstract: Machine learning for combinatorial optimization typically relies on neural constructors trained via reinforcement learning on large offline datasets for

Machines that know they are aging: a framework for hardware-aware autonomous intelligence

arXiv:2607.28451v1 Announce Type: new Abstract: Autonomous systems inevitably age, yet their artificial intelligence typically assumes hardware remains in its original condition. Batteries degrade, se

safetyarxiv-cs-ro

MeshFM: 2D Features Are All You Need for 3D Shape Understanding

arXiv:2607.27592v1 Announce Type: new Abstract: We present MeshFM, an efficient feedforward framework for extracting rich features from 3D inputs. Our method distills 2D features from visual foundatio

MUGEN: A Unified Framework for Efficient Motion Understanding and Generation

arXiv:2607.27581v1 Announce Type: new Abstract: Grounding human motion in language, and language in motion, is a central step toward physical AI systems that can understand, generate, and communicate

safetyarxiv-cs-lg

MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning

model-releasesarxiv-cs-ai

arXiv:2607.26465v1 Announce Type: new Abstract: Multimodal Large Language Models have sparked significant interest due to their potential for social intelligence; however, their ability to perform seq

Local Ai

Now You Have My Healthy Attention: A U-DiT for Brain-MRI Inpainting

arXiv:2607.27974v1 Announce Type: new Abstract: The ASNR-MICCAI BraTS Local Synthesis (Inpainting) task asks for the anatomically plausible completion of healthy brain tissue within a masked region of

local-aiarxiv-cs-cv

OPENAI IS GOING TO TAKE THIS ENTIRE MARKET DOWN WITH IT And you don't have to own a single share to get hurt. What I'm about to explain shou…

OPENAI IS GOING TO TAKE THIS ENTIRE MARKET DOWN WITH IT And you don't have to own a single share to get hurt. What I'm about to explain should worry anybody who thinks they're diversified: OpenAI is a

hardwareyann-lecun--x

Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork

arXiv:2607.27177v1 Announce Type: new Abstract: Effective collaboration with novel and diverse partners is a crucial skill for autonomous agents. Most current ad-hoc teamwork (AHT) approaches assume t

agentsarxiv-cs-ai

Psych-ECA: A Reproducible Semi-Synthetic Benchmark for Synthetic Control Arms in Longitudinal Psychiatry

model-releasesarxiv-cs-lg

arXiv:2607.27224v1 Announce Type: cross Abstract: External and synthetic control arms (ECAs) are entering psychiatric drug development, but the field lacks a benchmark that evaluates the properties re

Recall Before You Rank: Similarity-Guided Top-K Reuse for Efficient Long-Context Attention

arXiv:2607.27692v1 Announce Type: new Abstract: Top-K sparse attention reduces the cost of Softmax and value aggregation by attending to only a small subset of key--value (KV) entries. However, identi

researcharxiv-cs-cl

Tutorials

ReDiff: Reliability-Guided Diffusion for Trustworthy Ultra-Low-Field to High-Field MRI Synthesis

arXiv:2603.11325v2 Announce Type: replace Abstract: Low-field to high-field MRI synthesis has emerged as a promising strategy to improve image quality when access to high-field scanners is limited. Ho

tutorialsarxiv-cs-cv

RefineSVG: Visual Feedback-Driven Reinforcement Learning for Image-to-SVG Generation

arXiv:2607.27699v1 Announce Type: new Abstract: We propose RefineSVG, a single-step closed-loop visual feedback framework that enables multimodal large language models (MLLMs) to perform high-fidelity

agentsarxiv-cs-cv

ReGenVC: End-to-End Real-Time Generative Video Coding at Ultra-Low Bitrate

arXiv:2607.28144v1 Announce Type: cross Abstract: We present ReGenVC, an end-to-end generative video codec that compresses talking-head video to an ultra-low bitrate and decodes it in real time. The e

hardwarearxiv-cs-cv

ReToken: One Token to Improve Vision-Language Models for Visual Retrieval

arXiv:2607.28627v1 Announce Type: new Abstract: Long visual context poses a challenge for vision-language models: performance degrades as the number of distractors grows, and processing all tokens at

hardwarearxiv-cs-cv

RoboBRIDGE: A Modular Framework for Bridging Policies to Robust Real-World Robotic Agents

arXiv:2607.27881v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have attracted growing interest as a scalable approach to robotic manipulation. While these models are effective act

agentsarxiv-cs-ro

S-CEReBrO: Breaking the Memory Barrier in Continuous EEG Monitoring

arXiv:2607.27913v1 Announce Type: new Abstract: Foundation models offer a promising paradigm for Electroencephalography (EEG) analysis, leveraging generalizable representations from vast unlabeled dat

researcharxiv-cs-lg

SARC-DQ: Runtime Data-Quality Gating for Agentic AI: Silent Evidence Defects, the Incompetence Shield, and Downstream-Only Remediation

model-releasesarxiv-cs-ai

arXiv:2607.26313v1 Announce Type: cross Abstract: Agentic systems act, so a defect in the evidence they retrieve becomes a wrong action with a currency cost. The most dangerous enterprise defects are

SE(3)-MeanFlow: Few-Step Protein Backbone Generation on Lie Groups

arXiv:2607.27431v1 Announce Type: new Abstract: Generative modeling of protein backbones promises the de novo design of proteins with prescribed structural and functional properties. Existing diffusio

researcharxiv-cs-lg

ServerlessT2I: Efficient Text-to-Image Workflow Serving on a Serverless Platform

arXiv:2607.26566v1 Announce Type: cross Abstract: Text-to-image (T2I) workflows are increasingly deployed on serverless platforms because users often compose customized workflows and invoke them inter

safetyarxiv-cs-ai

Shared Semantic Codebook Distillation for Unpaired Cross-Modal Medical Classification

arXiv:2607.27357v1 Announce Type: new Abstract: Cross-modal knowledge distillation can transfer diagnostic knowledge from a strong but costly teacher modality to a cheaper and more deployable student

Stage-Replay Divergence Follows the KV Cache: Fixed-Prefix Precision Controls and Bidirectional Cache Transplantation

arXiv:2607.28495v1 Announce Type: cross Abstract: Stage-replay diagnostics reconstruct intermediate token prefixes and treat fresh-prefill continuation as continuation from the decoder state that orig

researcharxiv-cs-cl

Structuring Quantitative Image Analysis with Object Prominence

arXiv:2409.00216v2 Announce Type: replace Abstract: When photographers or media professionals compose an image, they make deliberate choices about what to foreground and what to background, shaping ho

TAPO: Transition-Aware Policy Optimization for LLM Agents

arXiv:2607.27973v1 Announce Type: new Abstract: Recently, Reinforcement Learning (RL) has emerged as a crucial paradigm for the post-training of Large Language Model (LLM) agents. However, existing me

safetyarxiv-cs-lg

Temporal Concentration from Rollout Errors: Implicit Preference Optimization for Text-to-Video Diffusion

arXiv:2607.28058v1 Announce Type: new Abstract: Recent advances in preference alignment for diffusion-based video generation, particularly via Direct Preference Optimization (DPO), have significantly

safetyarxiv-cs-cv

Thinking Once Is Enough: Intermediate-Layer Evidence Routing for High-Resolution VQA

model-releasesarxiv-cs-cv

arXiv:2607.27830v1 Announce Type: new Abstract: High-resolution visual question answering (HR-VQA) is often treated as a problem of insufficient evidence acquisition, where failing multimodal large la

Three-Photon Bayesian Imaging of Ortho-Positronium

arXiv:2607.27741v1 Announce Type: cross Abstract: PET provides functional images relying on two-photon coincidences from positron-electron annihilation. In human tissue, about 40% of annihilations are

Understanding Is Done Early: A Depth Division of Labor in Large Language Models and Its Use for Unbounded-Context Memory

arXiv:2607.28263v1 Announce Type: new Abstract: Transformer depth is not used uniformly: lower and middle layers build semantic representations, while upper layers increasingly specialize them for pre

hardwarearxiv-cs-cl

Understanding Submodular Information Measure Based Objectives for Representation Learning: A Variance and Separation Perspective

arXiv:2607.27660v1 Announce Type: cross Abstract: Submodular Information Measures (SIMs) have recently emerged as a powerful framework for representation learning and multimodal learning. In particula

verbalizing one of those aha moments i had that seems retroactively pretty obvious: if you prioritize pretrain data quality enough that comm…

verbalizing one of those aha moments i had that seems retroactively pretty obvious: if you prioritize pretrain data quality enough that commoncrawl isn't good enough for you, you have to build a Whole

agentsswyx--x

VideoCoCo: Code-as-CoT for Physically-Consistent Video Generation via an Agentic Dual-Engine System

arXiv:2607.27380v1 Announce Type: new Abstract: Text-to-video models have achieved remarkable visual quality, yet they still struggle to generate physically consistent dynamics because the temporal ev

agentsarxiv-cs-cv

Witness Evidence Portfolios: Single-Prefill Risk Detection for Closed Multimodal Answers

arXiv:2607.27667v1 Announce Type: new Abstract: Reliable deployment of multimodal large language models (MLLMs) requires deciding whether a confident visual answer should be trusted, reviewed, or rout

Would You Walk to the Car Wash? Revealing the Salience Bias of Large Language Models in Commonsense Reasoning

model-releasesarxiv-cs-cl

arXiv:2607.28478v1 Announce Type: new Abstract: As large language models (LLMs) continue to advance in complex reasoning tasks, they have learned to heavily prioritize explicit conditions provided in

Advancing the price-performance frontier with GPT‑5.6