Search: “evaluation”

RINO: Renormalization Group Distillation with No Labels

arXiv:2610.15919v1 Announce Type: cross Abstract: Supervised machine learning in high-energy physics (HEP) requires training on labeled simulations of particle collisions, which are known to be unfait

RM-OPD: On-Policy Distillation for Generative Reward Models

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DGX agent

arXiv:2610.20174v1 Announce Type: cross Abstract: Reinforcement Learning on Mixed-domain preference data (Mix-RL) has become the mainstream approach for training general-purpose Generative Reward Mode

safetyarxiv-cs-cl

15 Oct 2026

RouteWeaver: Weaving Mode Selection and Execution into Unified LLM Routing

arXiv:2610.19705v1 Announce Type: new Abstract: LLM routing dynamically selects and coordinates heterogeneous language models across diverse tasks. However, existing approaches are largely built aroun

safetyarxiv-cs-cl

S^3-Nav: Structured Spatial-Semantic Decision Making for Language-Guided Robot Navigation

arXiv:2610.19431v1 Announce Type: new Abstract: This paper presents S^3-Nav, a structured spatial-semantic decision-making framework for language-guided robot navigation under partial observability. R

safetyarxiv-cs-ro

SafeAdapt-Poker: Counterfactual Risk Calibration for Budgeted Adaptation under Opponent Shift

arXiv:2610.20473v1 Announce Type: cross Abstract: Opponent histories can mislead after behavioral shifts. SafeAdapt-Poker formulates budgeted adaptation under opponent shift as a dual-counterfactual s

local-aiarxiv-cs-lg

SafeDriveVLA: Navigation-Conditioned World Model Dreaming for Conflict-Aware End-to-End Autonomous Driving

model-releasesarxiv-cs-ro

arXiv:2610.20609v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are gaining attention in autonomous driving, inheriting commonsense priors from large language models (LLMs) and wid

Scaling Discovery Requires Forgetting

arXiv:2610.20563v1 Announce Type: cross Abstract: Automated research with LLM agents has shown promising results in various domains. Through iterative refinement and scored verifiers, LLM agents are c

agentsarxiv-cs-cl

Scaling Multilingual Mixture-of-Experts: How Language Coverage Changes Capacity and Compute Allocation

arXiv:2610.20247v1 Announce Type: new Abstract: Multilingual models share capacity across languages, so adding languages can hurt performance on existing ones. Mixture-of-experts models expand this ca

Scaling Personality Control in LLMs with Big Five Scaler Prompts

arXiv:2508.06149v2 Announce Type: replace Abstract: We present Big5-Scaler, a prompt-based framework for conditioning large language models (LLMs) with controllable Big Five personality traits. By emb

Scheduling Parallel Optical Circuit Switches for AI Training

arXiv:2603.07373v2 Announce Type: replace-cross Abstract: The rapid growth of AI training has dramatically increased datacenter traffic demand and energy consumption, which has motivated renewed inter

Seamless Deployment of Machine Learning Inference at the Far Edge: An Empirical AWS IoT Greengrass Architecture for Industrial Predictive Maintenance

arXiv:2610.14815v1 Announce Type: cross Abstract: Industrial IoT sensors produce continuous, high-volume data streams that predictive maintenance needs to analyse in real time. Cloud-centric architect

local-aiarxiv-cs-ai

Security in LLM-Assisted Coding: A Systematic Literature Review

arXiv:2610.15482v1 Announce Type: cross Abstract: Large Language Models accelerate software development but introduce severe security vulnerabilities. This systematic review investigates security risk

Self-Conditioning Enables Deep Sequential Reasoning in Flow Matching

arXiv:2610.19628v1 Announce Type: new Abstract: Recent work has explored visual reasoning through image generation, where models solve visual-reasoning problems by directly generating solution images.

researcharxiv-cs-cv

Self-Representation Alignment in Diffusion Models for Video Background Music Generation

arXiv:2610.15575v1 Announce Type: new Abstract: Diffusion models have been widely used for various generative tasks. However, recent studies have demonstrated that the internal representations learned

Applications

Semantics as a Shield: Label Disguise Defense (LDD) against Prompt Injection in LLM Sentiment Classification

arXiv:2511.21752v3 Announce Type: replace-cross Abstract: Prompt injection can cause large language models (LLMs) to deviate from their intended tasks by following instructions embedded in untrusted i

applicationsarxiv-cs-ai

Semi-Amortized Bayesian Inference with Reusable Local Posteriors

arXiv:2610.18945v1 Announce Type: cross Abstract: Amortized Bayesian inference uses simulations to perform Bayesian estimation when likelihoods are expensive or analytically unavailable. However, lear

Should SLMs be in the Driver's Seat? A Study of Local Models as Interpreters in Cloud-Written Agent Programs

arXiv:2610.14209v1 Announce Type: new Abstract: The most accurate agents executing user-provided tasks are driven by large, cloud-hosted models, which solve them by reading the private data of the use

local-aiarxiv-cs-ai

Similar Models, Broken Odds: Where Luce's Axiom Fails in Chatbot Arena

arXiv:2610.19095v1 Announce Type: new Abstract: Chatbot Arena ranks language models with the Bradley-Terry model, which gives each model one score and so assumes Luce's choice axiom. Choice theory pre

SkillFusion: Towards Reliable Multi-Skill Composition for LLM Agents

arXiv:2610.15481v1 Announce Type: new Abstract: Independently authored skills can impose incompatible requirements, competing preferences, and redundant instructions when jointly loaded by an LLM agen

agentsarxiv-cs-ai

Socratic Skill Learning: Interleaving Teaching and Practice for Transferable Agent Skills

arXiv:2610.17860v1 Announce Type: new Abstract: Agent Skills improve LLM agents by making task knowledge reusable across sessions. Learning transferable Skills requires evidence about a procedure and

agentsarxiv-cs-ai

Spacecraft Pose Tracking via Kinematic Prediction and Selective Event Correction

arXiv:2610.19465v1 Announce Type: new Abstract: In initialized spacecraft tracking, a short-horizon motion prediction remains available as event support changes. Observation-Aligned Event Innovation (

researcharxiv-cs-ro

SpecCloak: Probing Harmful Code Generation through Engineering Specifications

arXiv:2610.17907v1 Announce Type: cross Abstract: The code generation capabilities of large language models (LLMs) can equally be misused to implement harmful functionality, underscoring the need to u

safetyarxiv-cs-ai

SpecForge: Recursive Self-Improvement for Coding Agents via Behavioral Specifications

arXiv:2610.15708v1 Announce Type: new Abstract: Coding agents based on frozen language models can improve their performance by refining the external context they rely on, without updating model weight

Specification-Guided Synthesis of Deadlock-Free Communication Protocol Refinements with Large Language Models

arXiv:2607.27964v3 Announce Type: replace-cross Abstract: Ensuring behavioural correctness in communication protocols is a central challenge in distributed software systems, as subtle inconsistencies

State Representation Shapes Long-Horizon Agent Learning and Transfer: An Empirical Study

arXiv:2610.15407v1 Announce Type: new Abstract: An end-to-end success rate alone cannot distinguish failure to learn an action from failure to apply it as task state changes. We study this distinction

Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent

arXiv:2610.19187v1 Announce Type: cross Abstract: Full waveform inversion (FWI) is a powerful tool for high-resolution subsurface model reconstruction, but it remains highly nonlinear and ill-posed, e

Student Copula Markov Networks

arXiv:2610.19135v1 Announce Type: cross Abstract: This paper extends the applicability of probabilistic graphical models to non Gaussian data. It establishes a characterization of the pairwise Markov

SuPER-Mamba: Surprisal Prioritization for Extended-Context Routing in Mamba

arXiv:2610.16636v1 Announce Type: cross Abstract: State space models provide efficient linear-complexity processing for long sequences, but their performance frequently degrades as context length grow

SwarmRSI: Recursive Self-Improvement through Evolving Scientific Agent Swarm

arXiv:2610.20431v1 Announce Type: cross Abstract: Recursive self-improvement (RSI) in scientific research aims to improve agents' research capabilities through their own experimental experience. Scien

agentsarxiv-cs-cl

TEX-MEX: Similarity-Guided Temporal Contrastive Learning for Time Series

model-releasesarxiv-cs-lg

arXiv:2610.19557v1 Announce Type: new Abstract: Contrastive learning for time series depends on deciding which pairs of inputs should be treated as similar and dissimilar. Because few augmentations re

Text-Informed Training Data Filtering for Enhanced Virtual Flow Meter Performance

arXiv:2610.19860v1 Announce Type: cross Abstract: In sectors such as energy, aquaculture and chemical processing, the effectiveness of both mechanistic and data-driven models is frequently limited by

TextualRL: Textual Reinforcement Learning for Context Management

arXiv:2610.18216v1 Announce Type: cross Abstract: LLM agents can adapt to task feedback by revising the textual context that guides their behavior, without changing their model parameters. However, tu

TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Rendering

model-releasesarxiv-cs-cv

arXiv:2610.19551v1 Announce Type: new Abstract: Text-to-image models increasingly generate readable Chinese text that still contains local rendering defects, such as a character with an extra or missi

The Agentic Scientist: Orchestrating Coding Agents for Automated Scientific (Co-)Discovery

arXiv:2610.18933v1 Announce Type: cross Abstract: Modern coding agents are fundamentally reshaping the role of human input for computational research: they write code, launch in silico experiments, an

agentsarxiv-cs-cl

The Imperishable Record, Forgiveness, and the Future of Criminal Justice

arXiv:2610.15499v1 Announce Type: cross Abstract: Digital technologies can preserve records of wrongdoing indefinitely, and AI can give those records continuing influence over a person's future. We de

The Invisible Leash: Why RLVR May or May Not Escape Its Origin

model-releasesarxiv-cs-cl

arXiv:2507.14843v5 Announce Type: replace-cross Abstract: Recent advances highlight Reinforcement Learning with Verifiable Rewards (RLVR) as a promising approach to enhancing LLM capabilities. However

The Sharp Directions Are Against You: Curvature Analysis of Activation Steering

arXiv:2610.16084v1 Announce Type: new Abstract: Activation steering adds a vector to a language model's internal representations at inference time. It is a lightweight alternative to fine-tuning for b

The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale

arXiv:2610.18511v1 Announce Type: new Abstract: Buildings cause nearly 40% of carbon emissions in the US. Of total building emissions, HVAC systems are responsible for approximately 40-60% and account

safetyarxiv-cs-ai

Titan-Llama: Retrofitting Pre-trained LLMs with External Neural Memory Modules

model-releasesarxiv-cs-cl

arXiv:2610.19210v1 Announce Type: new Abstract: Transformers are efficient to pre-train at scale because attention permits parallel computation across sequence positions, but long-context inference re

Toward a Universal Characterization of Sudoku Puzzle Difficulty

arXiv:2507.21137v2 Announce Type: replace Abstract: In this paper we try to answer the question 'What constitutes Sudoku difficulty rating across different Sudoku websites?' Using two distinct methods

Toward Fine-Grained Forgetting:Attribute Unlearning for Multimodal Large Language Models

arXiv:2608.01008v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can memorize and disclose sensitive information, motivating machine unlearning to remove designated knowled

local-aiarxiv-cs-ai

Toward System One Clinical Intelligence: Parameter-Efficient Fine-Tuning and Counterfactual Explainability of Decision Foundation Models

arXiv:2610.15029v1 Announce Type: cross Abstract: Automated disease classification from patient-reported symptoms requires balancing the speed of lightweight discriminative models against the contextu

Towards Agentic Robots: Model, Data, Environment, and Harness

arXiv:2610.20442v1 Announce Type: new Abstract: Foundation models are driving a paradigm shift in robotic intelligence, moving beyond low-level action prediction to goal-directed orchestration of data

agentsarxiv-cs-ro

Trace-to-Architecture: Capability Bottleneck Diagnosis for Harness Revision

arXiv:2610.16725v1 Announce Type: new Abstract: Improving an LLM agent requires knowing what to change when it fails. Similar failures can require different fixes, and resolving one problem can expose

agentsarxiv-cs-ai

TRACER: Trace-Regularized Training Against Indirect Prompt Injection

arXiv:2610.19133v1 Announce Type: new Abstract: Indirect prompt injection uses untrusted retrieved content, tool outputs, or agent messages to override an LLM's intended behavior. We show that defense

agentsarxiv-cs-lg

Tracing Morphology, Semantics, and Divergence in Language Acquisition

arXiv:2610.19041v1 Announce Type: cross Abstract: Understanding the dynamics of language acquisition in Large Language Models (LLMs) is crucial for uncovering how statistical representations evolve in

Transparent Information Flow Modeling for Role-Aware AI Act Assessment

arXiv:2610.16916v1 Announce Type: new Abstract: The European Union's Artificial Intelligence Act introduces a structured framework for governing high-risk AI systems, emphasizing transparency, risk ma

safetyarxiv-cs-ai

Tread: A Visuo-Inertial Framework for Terrain and Locomotion Context-Aware Gait-Phase and Speed Estimation in Wearable Robotics

arXiv:2610.19067v1 Announce Type: new Abstract: A wearable assistive robot must continuously estimate terrain, locomotion mode, gait phase, and walking speed. These quantities are commonly addressed b

researcharxiv-cs-ro