Search: “evaluation”

Genesis Steering: Better Activation Steering Through Training Data

model-releasesarxiv-cs-lg

arXiv:2610.19114v1 Announce Type: new Abstract: Activation steering controls large language models at inference time, but its effectiveness depends on the choice of reference data. We introduce genesi

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GenState-AI: State-Aware Dataset for Text-to-Video Retrieval on AI-Generated Videos

DGX agent

arXiv:2603.14426v2 Announce Type: replace Abstract: Existing text-to-video retrieval benchmarks often permit matching from static appearance, making it difficult to determine whether models capture st

model-releasesarxiv-cs-cv

15 Oct 2026

GeoCloak: An Agentic Framework for Semantics-Preserving Geolocation Privacy via Peripheral Clue Manipulation

arXiv:2610.20055v1 Announce Type: new Abstract: The rapid advancement of large vision language models (LVLMs) has made geolocation inference from images highly accurate and accessible. This poses seri

agentsarxiv-cs-cv

Tutorials

GeoThinker-F: Endogenous Predictive World Representations for Fast Spatial Intelligence

arXiv:2610.20592v1 Announce Type: new Abstract: Existing geometry-aware Multimodal Large Language Models (MLLMs) increasingly rely on external 3D encoders to provide structural priors, but executing t

tutorialsarxiv-cs-cv

Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT

model-releasesarxiv-cs-cl

arXiv:2610.19043v1 Announce Type: cross Abstract: Because its binary optimization landscape contains exponentially many local optima, MAX-CUT is highly sensitive to initialization. We study Particle P

Graph-Enhanced Disease Trajectory Discovery and Temporal Endpoint Forecasting from Longitudinal Electronic Health Records

arXiv:2610.20502v1 Announce Type: new Abstract: Longitudinal electronic health records (EHRs) can reveal disease-progression structure, while useful forecasting requires leakage-controlled discovery,

researcharxiv-cs-lg

HERO: Harnessing Evidence for Test-time Refinement and Prompt Optimization in Program Generation

arXiv:2610.19184v1 Announce Type: new Abstract: Harness optimization improves LLMs by refining reusable prompts and scaffolding, yet requires further adaptation for program generation tasks such as te

Holding Agents Accountable with Verifiable Agent Insurance

arXiv:2610.18230v1 Announce Type: cross Abstract: While AI agents are useful for many tasks, they regularly misbehave in destructive ways, such as deleting important data. While prior work has focused

safetyarxiv-cs-ai

Holi-Spatial 360: Generative Reconstruction from Sparse Panoramas for Spatial Rendering and Intelligence

arXiv:2610.19502v1 Announce Type: new Abstract: Spatial intelligence requires scalable 3D environments that are both photorealistic and semantically grounded, yet existing indoor datasets are dominate

Local Ai

How Inner Optimizers Shape Replica Scaling

arXiv:2610.19064v1 Announce Type: new Abstract: Training large language models (LLMs) across geographically distributed and poorly interconnected compute pools or data centers has become a topic of si

local-aiarxiv-cs-lg

HyperLoop: Hyperspectral-Assisted Loop Closure Detection in 3D-SLAM for Planetary Exploration

arXiv:2610.19742v1 Announce Type: new Abstract: Autonomous navigation of planetary rovers is challenged by visually repetitive, featureless terrain. SLAM systems based on 3D Gaussian Splatting (3DGS)

agentsarxiv-cs-cv

Imagining Goals with Geometric Horizon Models for Policy Generalization

arXiv:2610.19129v1 Announce Type: new Abstract: Training from offline data has allowed for substantial progress in domains such as robotics, leading to general-purpose policies that can be easily appl

Infant Fingerprints: Quality Assessment & Recognition

arXiv:2610.20432v1 Announce Type: new Abstract: Infant fingerprint recognition is hindered by a combination of physiological characteristics, such as small ridge width and spacing, and acquisition cha

Input-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners

arXiv:2610.18547v1 Announce Type: new Abstract: People often tackle difficult problems by thinking more deeply or exploring more possibilities. Similarly, recursive reasoners, which reason by repeated

Instructions Are Evidence, Not Objectives: Measuring Outcome Alignment of Agents

arXiv:2610.17757v1 Announce Type: new Abstract: Large language model agents can follow complex human instructions to solve real-world tasks. In practice, however, initial instructions are often incomp

Integrated Novelty Detection Systems: From Sensor Networks to Multi-Novelty Computer Vision

arXiv:2610.19347v1 Announce Type: cross Abstract: Novelty detection is crucial in various technological and scientific domains. Its importance spans from ensuring the reliability of sensor networks to

researcharxiv-cs-cv

Hardware

Into the LidarVerse: A Real-Time Unified LiDAR World Model Across Tasks and Sensors

arXiv:2610.19201v1 Announce Type: new Abstract: We present LidarVerse, a real-time unified LiDAR world model for sensor simulation across tasks and sensors. Existing LiDAR simulators largely follow on

hardwarearxiv-cs-cv

Into the Unknown: Benchmarking AI agents on open scientific research problems

model-releasesarxiv-cs-cl

arXiv:2610.19159v1 Announce Type: cross Abstract: Can AI agents carry out research on open scientific problems? We introduce Into the Unknown, a benchmark that provides an agent with a science paper a

Introducing ExtractBenchPro: the most challenging benchmark for real-world document extraction. We see a massive long tail of document compl…

model-releasesjerry-liu--x

Introducing ExtractBenchPro: the most challenging benchmark for real-world document extraction. We see a massive long tail of document complexity in terms of use cases brought on from our customers, t

Introducing Muse Spark Open: the most capable US open-weight model to date

model-releasesr-localllama

Hey r/LocalLlama - Today we’re excited to release Muse Spark Open, the most capable US open-weight model to date. Built for coding and agentic work, it excels at long-horizon tasks, complex software e

Jeva: System One Models from First Principles

arXiv:2610.18773v1 Announce Type: new Abstract: System one models enable direct, low-latency decision-making. Yet recent practice of predicting an answer from serialized text does not, by itself, resp

Hardware

JointServe: Serving Multi-Model Agents on Memory-Constrained GPUs

arXiv:2610.19822v1 Announce Type: cross Abstract: LLM agents are evolving from a single model calling tools to planners that orchestrate multiple expert models whose capabilities live in distinct weig

hardwarearxiv-cs-lg

Kandinsky S2V 1.0: Audio-Driven Avatar Video Generation

arXiv:2610.19290v1 Announce Type: new Abstract: We present Kandinsky S2V 1.0, an open family of speech-to-video models for controllable audio-driven human animation, comprising Kandinsky S2V 1.0 Lite

Knowledge transfer between simple discrete worlds

arXiv:2610.15081v1 Announce Type: new Abstract: Adapting to a new world and reasoning correctly about which outcomes are possible under rules one has not met before is a basic capacity underlying gene

LaCache: Robust Semantic Caching for LLM Serving

arXiv:2608.01718v2 Announce Type: replace Abstract: Semantic caching, reusing responses to answer semantically similar queries, has seen growing adoption in LLM serving, offering faster responses and

Lag: A Mean-Field Optimal Policy for Online Assignment with Reusable Resources

arXiv:2610.19038v1 Announce Type: new Abstract: We study online task assignment with reusable resources. This problem arises in peer reviewer assignment, skill-based call routing, rideshare matching,

Large Decision Models: Repurposing Large Language Models as Single-Pass Decision Makers

arXiv:2610.15501v1 Announce Type: new Abstract: Large language models make decisions through parallel prefill and sequential decoding. Although effective, this approach incurs decoding costs and does

Large Language Model Assisted Transportation Planning through a Differentiable User Equilibrium Layer

arXiv:2610.16140v1 Announce Type: new Abstract: Transportation planning has traditionally required substantial expertise in network modeling, traffic assignment, and optimization. As a result, strateg

LATCH: LLM-Assisted Timing Closure Harness for Post-Route FPGA Optimisation

arXiv:2610.16625v1 Announce Type: new Abstract: Post-route FPGA timing closure remains expert-dependent and computationally expensive. We present LATCH (LLM-Assisted Timing Closure Harness), a two-lay

Learnable Hodge-Laplacian Driven Mixture of Experts for Steady-PDE Surrogates on Coarse Unstructured Meshes

arXiv:2610.20585v1 Announce Type: cross Abstract: Current approaches for steady partial differential equations surrogate models on unstructured meshes apply a single mixing mechanism to the whole solu

Learning by Examples: Power Grid Steady State Analysis with a Tabular Foundation Model

arXiv:2610.20462v1 Announce Type: new Abstract: Foundation models have shown strong generalization across scientific domains, but their use in power systems remains largely task-specific. We investiga

researcharxiv-cs-lg

Tutorials

Learning Keypoint Constraints from Visual Demonstrations for Safe Motion Planning

arXiv:2610.19295v1 Announce Type: new Abstract: We present Keypoint Constraint Learning (KCL), a framework for learning task-relevant motion constraints from visual demonstrations and for planning saf

tutorialsarxiv-cs-ro

Learning Proposition-Level Links from Sentence-Level Supervision for Neuro-Symbolic NLI

arXiv:2610.15158v2 Announce Type: replace Abstract: We study neuro-symbolic natural language inference (NLI) with independently translated propositional representations of the premise and hypothesis.

Learning Visual Speech Representations via Cross-Modal Distillation and Joint Face-Lip Modeling

arXiv:2610.19559v1 Announce Type: new Abstract: Lipreading remains challenging due to the inherent ambiguity of visual speech cues and their sensitivity to head pose and video resolution. In this work

Lifted State Policy Optimization: Internalizing Self-Correction through Transition Geometry

arXiv:2610.20350v1 Announce Type: cross Abstract: Preference optimization ranks final answers and trajectory RL maximizes aggregate returns, yet neither credits the local change that turns a flawed re

Likelihood Is Not Authorship: Can We Reliably Measure Human Contribution in AI-Assisted Writing?

arXiv:2610.19767v1 Announce Type: new Abstract: AI-text detection generally asks whether a machine wrote a text. As AI becomes an increasingly integral and accepted part of writing, the more relevant

Linearly Convergent Algorithms for Nonsmooth Problems with Unknown Smooth Regions

model-releasesarxiv-cs-lg

arXiv:2507.19465v2 Announce Type: replace-cross Abstract: We develop efficient algorithms for optimizing piecewise smooth (PWS) functions when their smooth regions are unknown to the algorithm. For co

Lingdan-V2: Large language models for clinically grounded reasoning in traditional Chinese medicine

arXiv:2610.14705v1 Announce Type: cross Abstract: Large language models have advanced medical reasoning, but clinically grounded reasoning in traditional Chinese medicine (TCM) remains challenging bec

Logit Leakage through Floating-Point Bias Addition

arXiv:2610.16790v1 Announce Type: cross Abstract: Language model APIs can expose token-specific biases while withholding logits and token probabilities. Existing bias-based extraction recovers relativ

Long-Horizon RL Needs Muon

arXiv:2610.20256v1 Announce Type: new Abstract: Long-horizon agents must coordinate reasoning, tool use, and environment feedback over extended interactions. Learning from sparse outcome rewards requi

Lotus: Learning to Understand Time-series by Learning to Forecast

arXiv:2610.18877v1 Announce Type: cross Abstract: Time-series language models typically align numerical signals with language using labels, captions, or question-answer pairs. Although such supervisio

LYNX: Analytical Program Synthesis via Grounding and Closed-Loop Refinement

arXiv:2610.14777v1 Announce Type: new Abstract: LLM-powered data analytics aims to translate natural language instructions into executable analytical programs over underlying data. However, existing a

MADDIE: Multi-Agent Drug Discovery Engine

arXiv:2610.16975v1 Announce Type: new Abstract: Drug discovery remains a resource-intensive and time-consuming process, involving billions of dollars and a decade of development to advance a drug to m

Making Language the Low-Rate Code of Vision: Successive Refinement from Words to Pixels

arXiv:2610.16552v1 Announce Type: new Abstract: An ordered visual code lets a short prefix stand in for an image, and the training loss decides what that prefix contains. Under pixel reconstruction th

MCAIR: Monitoring and Correcting Agent Unfaithfulness via Internal Representations

arXiv:2610.19679v1 Announce Type: cross Abstract: Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in completing complex tasks through multi-step reasoning and tool in

agentsarxiv-cs-lg

Measuring the Inference Energy Efficiency of Open-Weight LLMs across Benchmarks, Reasoning Modes, and Hardware

arXiv:2610.14389v1 Announce Type: new Abstract: Benchmark scores tell us what an LLM can do, but not how much energy is required to deliver that capability. We directly measure this trade-off for 86 o

MemDuet: Learning Dual-Process Memory for Long-Context Reasoning

arXiv:2610.19928v1 Announce Type: new Abstract: Reasoning over infinitely long documents is becoming a reality with recurrent memory agents. However, increasing context length exacerbates the trade-of

safetyarxiv-cs-cl

MemeTrace: A Multimodal LLM-Based Agent for Safety-Aware Meme Provenance Analysis

arXiv:2610.19267v1 Announce Type: cross Abstract: Online memes are often carriers of unsafe content such as disinformation and propaganda. Their rapid evolution in response to news events and strong d

safetyarxiv-cs-cl