Search: “evaluation”

Scaling Vision Models Does Not Consistently Improve Localisation-Based Explanation Quality

arXiv:2605.10142v1 Announce Type: cross Abstract: Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of po

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Model Releases

Scam2Prompt: A Scalable Framework for Auditing Malicious Scam Endpoints in Production LLMs

DGX agent

arXiv:2509.02372v3 Announce Type: replace-cross Abstract: Large Language Models have become critical to modern software development, but their reliance on uncurated web-scale datasets for training int

model-releasesarxiv-cs-ai

12 May 2026

ScholarPeer: A Context-Aware Multi-Agent Framework for Automated Peer Review

arXiv:2601.22638v2 Announce Type: replace-cross Abstract: The exponential growth of machine learning submissions has strained the traditional peer review process, resulting in slow feedback loops for

Scratchpad Patching: Decoupling Compute from Patch Size in Byte-Level Language Models

arXiv:2605.09630v1 Announce Type: new Abstract: Tokenizer-free language models eliminate the tokenizer step of the language modeling pipeline by operating directly on bytes; patch-based variants furth

researcharxiv-cs-cl

Tutorials

SE-Bench: Benchmarking Self-Evolution with Knowledge Internalization

arXiv:2602.04811v2 Announce Type: replace-cross Abstract: True self-evolution requires agents to act as lifelong learners that internalize novel experiences to solve future problems. However, rigorous

tutorialsarxiv-cs-ai

SeBA: Semi-supervised few-shot learning via Separated-at-Birth Alignment for tabular data

arXiv:2605.08519v1 Announce Type: new Abstract: Learning from scarce labeled data with a larger pool of unlabeled samples, known as semi-supervised few-shot learning (SS-FSL), remains critical for app

Seed Hijacking of LLM Sampling and Quantum Random Number Defense

arXiv:2605.08313v1 Announce Type: cross Abstract: Large language models (LLMs) rely on deterministic pseudorandom number generators (PRNGs) for autoregressive sampling, creating a critical supply-chai

SEMASIA: A Large-Scale Dataset of Semantically Structured Latent Representations

arXiv:2605.09485v1 Announce Type: new Abstract: Latent representations learned by neural networks often exhibit semantic structure, where concept similarity is reflected by geometric proximity in embe

Sens-VisualNews: A Benchmark Dataset for Sensational Image Detection

model-releasesarxiv-cs-cv

arXiv:2605.10394v1 Announce Type: new Abstract: The detection of sensational content in media items can be a critical filtering mechanism for identifying check-worthy content and flagging potential di

Sequential Feature Selection for Efficient Landslide Segmentation from Multi-Spectral Data

arXiv:2605.09746v1 Announce Type: cross Abstract: Landslide detection from satellite imagery has advanced through deep learning, yet most models rely on large, highly correlated spectral-topographic i

ShadowMerge: A Novel Poisoning Attack on Graph-Based Agent Memory via Relation-Channel Conflicts

arXiv:2605.09033v1 Announce Type: cross Abstract: Graph-based agent memory is increasingly used in LLM agents to support structured long-term recall and multi-hop reasoning, but it also creates a new

Shields to Guarantee Probabilistic Safety in MDPs

arXiv:2605.10888v1 Announce Type: cross Abstract: Shielding is a prominent model-based technique to ensure safety of autonomous agents. Classical shielding aims to ensure that nothing bad ever happens

SkillMaster: Toward Autonomous Skill Mastery in LLM Agents

arXiv:2605.08693v1 Announce Type: new Abstract: Skills provide an effective mechanism for improving LLM agents on complex tasks, yet in existing agent frameworks, their creation, refinement, and selec

SLayerGen: a Crystal Generative Model for all Space and Layer Groups

arXiv:2605.08262v1 Announce Type: cross Abstract: Crystal generative models have shown rapid progress for accelerating the discovery of bulk, periodic materials. However, many material systems such as

researcharxiv-cs-ai

SlimSpec: Low-Rank Draft LM-Head for Accelerated Speculative Decoding

arXiv:2605.10453v1 Announce Type: cross Abstract: Speculative decoding speeds up autoregressive generation in Large Language Models (LLMs) through a two-step procedure, where a lightweight draft model

researcharxiv-cs-cl

Local Ai

Smart Railway Obstruction Detection System using IoT and Computer Vision

arXiv:2605.08246v1 Announce Type: new Abstract: Railway track intrusions pose a critical safety challenge for Indian Railways, encompassing wildlife incursions and deliberate malicious obstructions. T

local-aiarxiv-cs-cv

SoK: A Systematic Bidirectional Literature Review of AI & DLT Convergence

arXiv:2605.10515v1 Announce Type: cross Abstract: The integration of Artificial Intelligence (AI) with Distributed Ledger Technology (DLT) has become a growing research area, yet contributions tend to

Spatial-Frequency Gated Swin Transformer for Remote Sensing Single-Image Super-Resolution

arXiv:2605.09687v1 Announce Type: new Abstract: Remote Sensing (RS) single-image super-resolution aims to reconstruct high-resolution imagery from low-resolution observations while preserving fine spa

researcharxiv-cs-cv

SpectraLLM: Uncovering the Ability of LLMs for Molecular Structure Elucidation from Multi-Spectral Data

arXiv:2508.08441v3 Announce Type: replace-cross Abstract: Automated molecular structure elucidation remains challenging, as existing approaches often depend on pre-compiled databases or restrict thems

Applications

SPECTRE: Hybrid Ordinary-Parallel Speculative Serving for Resource-Efficient LLM Inference

arXiv:2605.08151v1 Announce Type: cross Abstract: LLM serving platforms are increasingly deployed as multi-model cloud systems, where user demand is often long-tailed: a few popular large models recei

applicationsarxiv-cs-ai

Split CNN Inference on Networked Microcontrollers

arXiv:2605.09357v1 Announce Type: cross Abstract: Running deep neural networks on microcontroller units (MCUs) is severely constrained by limited memory resources. While TinyML techniques reduce model

researcharxiv-cs-lg

Statistical Scouting Finds Debate-Safe but Not Debate-Useful Cases: A Matched-Ceiling Study of Open-Weight LLM Reasoning Protocols

arXiv:2605.09618v1 Announce Type: new Abstract: When should a language model answer directly, sample and vote, or engage in multi-agent debate? Recent work shows voting often explains much of the gain

Step Rejection Fine-Tuning: A Practical Distillation Recipe

arXiv:2605.10674v1 Announce Type: cross Abstract: Rejection Fine-Tuning (RFT) is a standard method for training LLM agents, where unsuccessful trajectories are discarded from the training set. In the

Structure-Preserving Reconstruction of Convex Lipschitz Functionals on Hilbert Spaces from Finite Samples

arXiv:2605.08559v1 Announce Type: cross Abstract: Convex functionals are ubiquitous in applied analysis, appearing as value functions, risk measures, super-hedging prices, and loss functionals in mach

Supervised Dimensionality Reduction Revisited: Why LDA on Frozen CNN Features Deserves a Second Look

arXiv:2604.03928v2 Announce Type: replace-cross Abstract: Frozen pretrained image representations are widely used for transfer learning: a backbone is kept fixed, feature vectors are extracted, and a

researcharxiv-cs-ai

Supervised Mixture-of-Experts for Surgical Grasping and Retraction

arXiv:2601.21971v2 Announce Type: replace-cross Abstract: Imitation learning has achieved remarkable success in robotic manipulation, yet its application to surgical robotics remains challenging due t

Synergistic Simplex: Cooperative Runtime Assurance for Safety-Critical Autonomous Systems

arXiv:2605.08190v1 Announce Type: new Abstract: Autonomous systems increasingly rely on machine-learning (ML) components for safety-critical tasks such as perception and control in autonomous vehicles

safetyarxiv-cs-lg

Tabular Foundation Model for Generative Modelling

arXiv:2605.09424v1 Announce Type: new Abstract: Generative modelling is a demanding test of foundation models, because it requires robust, holistic representation learning for a given data modality, r

Talk to Your Slides: High-Efficiency Slide Editing via Language-Driven Structured Data Manipulation

arXiv:2505.11604v5 Announce Type: replace Abstract: Editing presentation slides is a frequent yet tedious task, ranging from creative layout design to repetitive text maintenance. While recent GUI-bas

TARO: Temporal Adversarial Rectification Optimization Using Diffusion Models as Purifiers

arXiv:2605.08440v1 Announce Type: cross Abstract: Adversarial purification with diffusion models seeks to project adversarial examples back toward the data manifold, but balancing semantic preservatio

researcharxiv-cs-cv

Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning

arXiv:2605.10247v1 Announce Type: new Abstract: Using Large Language Models (LLMs) to process graph-structured data is an active research area, yet current state-of-the-art approaches typically rely o

Applications

Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Models

arXiv:2512.13618v3 Announce Type: replace Abstract: Representing continuous time is a critical and under-explored challenge in modeling temporal event sequences with large language models (LLMs). Vari

applicationsarxiv-cs-cl

The Attacker in the Mirror: Breaking Self-Consistency in Safety via Anchored Bipolicy Self-Play

arXiv:2605.08427v1 Announce Type: new Abstract: Self-play red team is an established approach to improving AI safety in which different instances of the same model play attacker and defender roles in

The Bystander Effect in Multi-Agent Reasoning: Quantifying Cognitive Loafing in Collaborative Interactions

arXiv:2605.10698v1 Announce Type: cross Abstract: Multi-agent systems (MAS) assume that collaborating inherently improves Large Language Model (LLM) reasoning. We challenge this by demonstrating that

The Differences Between Direct Alignment Algorithms are a Blur

arXiv:2502.01237v3 Announce Type: replace Abstract: Direct Alignment Algorithms (DAAs) simplify LLM alignment by directly optimizing policies, bypassing reward modeling and RL. While DAAs differ in th

The Extrapolation Cliff in On-Policy Distillation of Near-Deterministic Structured Outputs

arXiv:2605.08737v1 Announce Type: cross Abstract: On-policy distillation (OPD) is widely used for LLM post-training. When pushed with a reward-extrapolation coefficient lambda > 1, the student can lif

The Metacognitive Probe: Five Behavioural Calibration Diagnostics for LLMs

arXiv:2605.09844v1 Announce Type: new Abstract: The Metacognitive Probe is an exploratory five-task, 15-slot diagnostic that decomposes an LLM's confidence behaviour into five behaviourally-distinct d

The Realignment Problem: When Right becomes Wrong in LLMs

arXiv:2511.02623v2 Announce Type: replace Abstract: Post-training alignment of large language models (LLMs) relies on large-scale human annotations guided by policy specifications that change over tim

The Reciprocity Gradient

arXiv:2605.08323v1 Announce Type: cross Abstract: Communication is fundamental to sustaining reciprocity and cooperation in strategic interactions. We identify and formulate the influence attribution

The Safety-Aware Denoiser for Text Diffusion Models

arXiv:2605.08116v1 Announce Type: cross Abstract: Recent work on text diffusion models offers a promising alternative to autoregressive generation, but controlling their safety remains underexplored.

The Wristband Gaussian Loss: Deterministic, Composable Latents via a Sphere-Interval Decomposition

arXiv:2605.08749v1 Announce Type: new Abstract: We present the Wristband Gaussian Loss, a deterministic batch loss for Gaussianizing point embeddings without sampling, KL terms, or iterative transport

ThreatCore: A Benchmark for Explicit and Implicit Threat Detection

arXiv:2605.10563v1 Announce Type: cross Abstract: Threat detection in Natural Language Processing lacks consistent definitions and standardized benchmarks, and is often conflated with broader phenomen

TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning

arXiv:2605.10038v1 Announce Type: new Abstract: Time series analysis underpins forecasting, monitoring, and decision making in domains such as finance and weather, where solving a task often requires

TinyTroupe: An LLM-powered Multiagent Persona Simulation Toolkit

arXiv:2507.09788v2 Announce Type: replace-cross Abstract: Recent advances in Large Language Models (LLM) have led to a new class of autonomous agents, renewing and expanding interest in the area. LLM-

Token Economics for LLM Agents: A Dual-View Study from Computing and Economics

arXiv:2605.09104v1 Announce Type: new Abstract: As LLM agents evolve, tokens have emerged as the core economic primitives of Agentic AI. However, their exponential consumption introduces severe comput

ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering

arXiv:2510.20036v2 Announce Type: replace Abstract: Large language model (LLM) agents rely on external tools to solve complex tasks, but real-world toolsets often contain redundant tools with overlapp

agentsarxiv-cs-cl

TopoU-Net: a U-Net architecture for topological domains

arXiv:2605.10091v1 Announce Type: new Abstract: Many modern datasets mix points, edges, regions, groups, objects, events, hyperedges, and relations. Yet neural architectures often force such data into

researcharxiv-cs-lg

Toward an Engineering of Science: Rebalancing Generation and Verification in the Age of AI

arXiv:2605.10425v1 Announce Type: cross Abstract: AI systems can now cheaply generate plausible scientific artifacts such as papers, reviews, and surveys. This creates a risk of epistemic pollution in

researcharxiv-cs-ai