Search: “evaluation”

A Framework for Egocentric and Exocentric Procedural Understanding via Temporal Segmentation and Semantic Abstraction

arXiv:2610.00069v1 Announce Type: cross Abstract: Long-horizon ego/exo data contains rich procedural evidence, but are redundant, noisy, and costly to process or retain. We propose a compact framework

local-aiarxiv-cs-ai

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A Large Scale Investigation of Scaling Limits in Chemical Language Models

DGX agent

arXiv:2508.13408v3 Announce Type: replace Abstract: Chemical Language Models (CLMs) are increasingly used in de novo drug design, driven by recent growth in model scale, compute, and dataset size. How

tutorialsarxiv-cs-lg

2 Oct 2026

A Multi-Agent LLM Framework for Personalized Health Checkup Interpretation and Guidance

arXiv:2610.01451v1 Announce Type: new Abstract: Personalized interpretation of health checkup results requires reasoning across longitudinal records, medical knowledge, lifestyle guidance, and healthc

A Safe Prototype Is Not a Safety Direction: Reference Dependence and Prompt Confounds in Response-Safety Embeddings

arXiv:2610.01801v1 Announce Type: new Abstract: Can response safety be scored by cosine similarity to the mean embedding of known-safe responses? A recent sleeper-agent detector proposes exactly this

safetyarxiv-cs-lg

AbsorbEvo: An Agentic Framework for Autonomous Inverse Design of Microwave Absorbers

arXiv:2610.01119v1 Announce Type: new Abstract: Designing high-performance microwave absorbers requires specialized expertise in electromagnetic theory, materials science and simulation programming, a

AI-assisted mitotic counting improves reproducibility and efficiency across multiple tumour types

arXiv:2610.01813v1 Announce Type: new Abstract: Mitotic counting is an important component of tumour grading, diagnosis and prognostic assessment across several tumour types, but manual assessment is

Analysis of Quantized and Efficiently Adapted Protein Language Models

arXiv:2610.00665v1 Announce Type: new Abstract: Background: Protein language models (PLMs) are increasingly used for sequence generation and property prediction, but their size makes fine-tuning and d

AnchorPrompt: Self-Distilled Soft Prompts for Robust Audio-Language Models

arXiv:2610.00706v1 Announce Type: cross Abstract: Large audio-language models (LALMs) are sensitive to input perturbations, such as noise, waveform corruption, and adversarial injections. We propose A

Anomaly Detection and Localization for the Pantograph-Catenary System

arXiv:2610.01721v1 Announce Type: new Abstract: Monitoring the Pantograph-Catenary System (PCS) provides insight into the health conditions of the pantograph and the railway infrastructure. Recent ind

safetyarxiv-cs-lg

Anti-Persona: Disrupting Unauthorized Identity Binding and Recognition in Personalized Vision--Language Models

arXiv:2610.01944v1 Announce Type: new Abstract: Few-shot personalization enables large vision--language models (LVLMs) to learn user-specific visual concepts for applications such as personalized retr

safetyarxiv-cs-cv

Authorization for Self-Modifying AI Agent Populations: Conserving Authority across Replacement, Forking, and Rollback

arXiv:2610.00347v1 Announce Type: cross Abstract: Self-modifying AI agents can replace, fork, and roll back identity-bearing software while descendants remain executable. Per-successor authorization d

agentsarxiv-cs-ai

Backdoor Purification for LoRA-Tuned LLMs via Null-Space Projection

arXiv:2610.00685v1 Announce Type: new Abstract: With the rapid adoption of large language models (LLMs) and parameter-efficient fine-tuning (PEFT) methods, the risk of backdoor attacks has become more

Beyond Domain-Level Adaptation: Margin-Oriented Semantic-Appearance Interaction Correction for Personalized Federated Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2610.01625v1 Announce Type: new Abstract: Federated parameter-efficient fine-tuning enables distributed clients to adapt pretrained vision-language models without sharing raw data or updating th

Beyond State-of-the-Art: Standardising Environmental Impact Metrics for AI Research

arXiv:2610.01116v1 Announce Type: new Abstract: As the capabilities and ubiquity of Large Language Models (LLMs) grow, so does their environmental footprint. Despite calls for responsible AI, the mach

BG4Sea: Biogeochemical Seasonal Forecastability via Progressive Information Scaling

arXiv:2607.16731v2 Announce Type: replace Abstract: Marine biogeochemical forecasting is increasingly important for managing marine ecosystems and the carbon cycle, yet global, seasonal forecast produ

researcharxiv-cs-lg

Bootstrapping Video Interaction Generation with Synthetic State Transitions

arXiv:2610.01039v1 Announce Type: new Abstract: While recent video generative models can synthesize high-fidelity videos, they struggle to portray plausible physical interactions and the resulting sta

researcharxiv-cs-cv

Build2SPARQL: A Large-Scale Text-to-SPARQL Benchmark Dataset for Building Knowledge Graph Querying

arXiv:2610.00224v1 Announce Type: new Abstract: Building automation systems are increasingly represented as semantic knowledge graphs (KGs) using ontologies such as Brick and ASHRAE 223P, creating a m

Calibrating Prediction Timeliness Through Multi-Objective Hyperparameter Optimization for Remaining Useful Life Prediction

arXiv:2610.01530v1 Announce Type: new Abstract: In predictive maintenance, early and late RUL prediction errors carry asymmetric consequences, yet hyperparameter optimization typically targets a singl

researcharxiv-cs-lg

Calibration-risk routing for controlled world-model adaptation

arXiv:2610.01001v1 Announce Type: new Abstract: Model-based reinforcement learning (MBRL) can exploit simulated experience, but a simulator-to-target shift creates a model-selection problem: correctin

Can AI Scientists Coordinate at Runtime?

arXiv:2610.00980v1 Announce Type: cross Abstract: Multi-agent AI scientists have shown improving performance across a diverse range of tasks. Yet a common approach is design-time agentic orchestration

agentsarxiv-cs-ai

CARM: Cancellation-Aware Response Masking for LLM Reinforcement Learning

arXiv:2610.02039v1 Announce Type: cross Abstract: Recent years have witnessed the rapid adoption of reinforcement learning (RL) in large language model (LLM) post-training, with substantial gains in m

Hardware

CAST: Cost-Aware Speculative Trees from One-Pass Block Drafters

arXiv:2610.00321v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model inference by drafting future tokens cheaply and verifying them with the target model in parallel

hardwarearxiv-cs-lg

Certainty Is Not Just Correctness: Rethinking Token-Level Certainty in LLM Reasoning

arXiv:2610.00296v1 Announce Type: cross Abstract: Token-level certainty is widely used as a proxy for correctness in LLM training and inference. However, the performance of certainty-based methods dep

local-aiarxiv-cs-ai

Chaining Skills to Hijack LLM Agents

arXiv:2610.01564v1 Announce Type: cross Abstract: LLM agents use skills to improve performance on specialized tasks. To complete a user request, an agent may invoke several skills in sequence, allowin

CLASP: Continual Low-rank Adapters for Spatially Placed Concepts from One Hypernetwork

model-releasesarxiv-cs-cv

arXiv:2610.01331v1 Announce Type: new Abstract: Continual personalization of text-to-image diffusion models requires sequentially acquiring new concepts while retaining previously learned ones. Howeve

Classical Hardness of Learning Functions of Hamiltonians

arXiv:2610.01141v1 Announce Type: cross Abstract: Morohoshi, Nakayama, Manabe, and Mitarai proposed a physically motivated quantum machine learning problem in which the goal is to predict quantities o

researcharxiv-cs-lg

CoEvolve: Construct-to-Edit Visual Grounding with Bidirectional State Refinement

arXiv:2610.01710v1 Announce Type: new Abstract: Visual grounding localizes an object described by language with a bounding box. Most multimodal grounding models compress target identification, spatial

Cog-VADU: A Training-Free Cognitive Reasoning Framework for Video Anomaly Detection and Understanding

arXiv:2610.01754v1 Announce Type: cross Abstract: Video Anomaly Detection (VAD) aims to temporally localize abnormal events in videos. Most existing approaches rely on dataset-specific training and cu

local-aiarxiv-cs-ai

CommunityKV: Efficient Long-Context Decoding via Graph Partitioning

arXiv:2610.00418v1 Announce Type: new Abstract: Scaling Transformers to long contexts is constrained by the quadratic cost of self-attention and the linear growth of key-value cache memory transfer. S

Comparing a gradient boosting algorithm to the GOES FDC for wildfire detection

arXiv:2610.01994v1 Announce Type: new Abstract: Wildfires pose severe risks to human life, ecosystems, and property. This study presents a machine learning approach for wildfire detection from GOES AB

researcharxiv-cs-cv

Constant-Time Planning for Chaining Collision-free Motion to Manipulation Behaviors

arXiv:2512.00939v3 Announce Type: replace-cross Abstract: Recent progress in contact-rich robotic manipulation has been striking, yet most deployed systems remain confined to simple, scripted routines

Contextual trajectory and incremental contextual displacement: Towards using LLMs to understand dynamic, utterance-specific meaning construction

arXiv:2610.00840v1 Announce Type: cross Abstract: Transformer-based large language models (LLMs) such as RoBERTa represent text using contextual word embeddings (CWEs), which alter the embeddings asso

Contingent Exposure Routing for Financial AI: Outage Risk and the Cost of Indivisible Decisions

arXiv:2610.00239v1 Announce Type: new Abstract: Model failover restores availability, but changes which financial institutions share decision errors. We formulate outage-contingent routing through a l

agentsarxiv-cs-lg

Applications

Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrations

arXiv:2610.01301v1 Announce Type: cross Abstract: Most current grasp synthesis systems are trained offline and remain fixed during deployment. While this works well when deployment conditions resemble

applicationsarxiv-cs-lg

Continuous Conditioning of VLAs with Augmenting EMG and Visual Task Descriptors

arXiv:2610.01794v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models rely strongly on language for describing task information, despite having multimodal inputs. We hypothesize that oth

researcharxiv-cs-cv

Cross-Benchmark Transfer from RL on Agentic Coding Tasks

arXiv:2610.00890v1 Announce Type: cross Abstract: Coding agents often fail in the last mile: they build most of a feature but drop a requirement, test only the cases their implementation already handl

Cross-entropy optimization with prioritized constraints

arXiv:2610.01319v1 Announce Type: new Abstract: When constraints conflict, an optimizer must determine which requirements to preserve and which to relax. On the one hand, a priority ordering specifies

researcharxiv-cs-ro

Cross-Lingual Alignment for Decoder-Only Models using MoE Routers

arXiv:2610.01921v1 Announce Type: cross Abstract: Cross-lingual contrastive learning has been a core component of multilingual encoder training, but the ability to explicitly align representations is

CrossGMN: Graph Metanetworks for Cross-Architecture Weight-Space Transformations

arXiv:2610.01649v1 Announce Type: new Abstract: Weight-space networks operate directly on parameters of other neural networks, enabling tasks such as predicting model properties, editing trained model

Dataset Identity, Not Novelty: The Source of an Inflated OOD Detection Gain

model-releasesarxiv-cs-cv

arXiv:2610.01096v1 Announce Type: cross Abstract: A post-hoc out-of-distribution (OOD) detector reads the activations of a trained classifier and returns a score. It fits that score on in-distribution

DAYJOB: A Benchmark for Long-Horizon Professional Work

arXiv:2610.01306v1 Announce Type: new Abstract: Professional work often starts with a brief request that leaves the professional to work out what is needed, which documents matter, and whether the req

Decentralized Safe Path Following for Multiple Quadrotors on Intersecting Paths with Theoretical Guarantees

arXiv:2610.00208v1 Announce Type: cross Abstract: This paper studies decentralized safe path following for multiple quadrotors on intersecting paths, where safety requires both collision avoidance and

safetyarxiv-cs-ro

Decoding the Disaster: Multi-Task Geospatial Reasoning with Vision-Language Models and Crowdsourced Imagery for Disaster Mapping

arXiv:2610.00302v1 Announce Type: cross Abstract: Crowdsourced imagery provides timely, fine-grained, street-level observations for disaster mapping, complementing conventional remote sensing imagery

Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey

arXiv:2610.00363v1 Announce Type: cross Abstract: Ensuring safe and reliable operation of modern railway systems increasingly relies on data-driven monitoring and intelligent fault detection. Deep lea

DeFA: Dependency-Guided Failure Attribution for LLM Agents

arXiv:2610.01256v1 Announce Type: new Abstract: Errors in LLM agent executions and their visible consequences can be separated by many steps, making decisive-error localization a matter of understandi

agentsarxiv-cs-ai

Degree-Corrected Joint Matrix Factorization for Multilayer Community Detection

arXiv:2610.01361v1 Announce Type: cross Abstract: Multilayer networks allow the modeling of interactions between the same entities across different contexts, such as temporal observations, varying set

local-aiarxiv-cs-lg

Distilling Directional Verification

arXiv:2610.00997v1 Announce Type: cross Abstract: Knowledge distillation aims to transfer the factual knowledge of large language models to smaller models for efficient deployment. Yet a teacher may r

Do Vision Language Models Understand Human Engagement in Games?

arXiv:2603.18480v2 Announce Type: replace-cross Abstract: Inferring human engagement from gameplay video is important for game design and player-experience research, yet it remains unclear whether vis