Search: “evaluation”

Pretty Good Measurement for Radiomics: A Quantum-Inspired Multi-Class Classifier for Lung Cancer Subtyping and Prostate Cancer Risk Stratification

arXiv:2603.00223v2 Announce Type: replace Abstract: We investigate a quantum-inspired approach to supervised multi-class classification based on the Pretty Good Measurement (PGM), viewed as an operato

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Probabilistic Object Detection with Conformal Prediction

DGX agent

arXiv:2605.07549v1 Announce Type: new Abstract: Conformal Prediction (CP) is a distribution-free method for constructing prediction sets with marginal finite-sample coverage guarantees, making it a su

safetyarxiv-cs-cv

11 May 2026

Prompt Engineering Strategies for LLM-based Qualitative Coding of Psychological Safety in Software Engineering Communities: A Controlled Empirical Study

arXiv:2605.07422v1 Announce Type: cross Abstract: Qualitative analysis plays a pivotal role in understanding the human and social aspects of software engineering. However, it remains a demanding proce

Tutorials

PropSplat: Map-Free RF Field Reconstruction via 3D Gaussian Propagation Splatting

arXiv:2605.08035v1 Announce Type: cross Abstract: Building a site-specific propagation model typically requires either ray-tracing over detailed 3D maps or dense measurement campaigns. Both approaches

tutorialsarxiv-cs-lg

ProtoSSL: Interpretable Prototype Learning from Unlabeled Time-Series Data

arXiv:2605.06943v1 Announce Type: new Abstract: In time-series domains where both predictive performance and interpretability are essential, deep neural networks achieve strong results but provide lim

ProtSent: Protein Sentence Transformers

arXiv:2605.06830v1 Announce Type: cross Abstract: Protein language models (pLMs) produce per-residue representations that capture evolutionary and structural information, yet their mean-pooled sequenc

researcharxiv-cs-cl

Prune-OPD: Efficient and Reliable On-Policy Distillation for Long-Horizon Reasoning

arXiv:2605.07804v1 Announce Type: cross Abstract: On-policy distillation (OPD) leverages dense teacher rewards to enhance reasoning models. However, scaling OPD to long-horizon tasks exposes a critica

Quality-Conditioned Agreement in Automated Short Answer Scoring: Mid-Range Degradation and the Impact of Task-Specific Adaptation

arXiv:2605.07647v1 Announce Type: cross Abstract: Automated short answer scoring (ASAS) is shifting from discriminative, fine-tuned models to large language models (LLMs) used in few-shot settings. Th

Radiologist-Guided Causal Concept Bottleneck Models for Chest X-Ray Interpretation

arXiv:2605.07785v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) in medical imaging aim to improve model interpretability by predicting intermediate clinical concepts before final diag

safetyarxiv-cs-cv

Randomness is sometimes necessary for coordination

arXiv:2605.06825v1 Announce Type: new Abstract: Full parameter sharing is standard in cooperative multi-agent reinforcement learning (MARL) for homogeneous agents. Under permutation-symmetric observat

RCoT-Seg: Reinforced Chain-of-Thought for Video Reasoning and Segmentation

arXiv:2605.07334v1 Announce Type: new Abstract: Video Reasoning Segmentation (VRS) aims to segment target objects in videos based on implicit instructions that convey human intent and temporal logic.

Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners

arXiv:2605.08019v1 Announce Type: new Abstract: Humans rapidly learn abstract knowledge when encountering novel environments and flexibly deploy this knowledge to guide efficient and intelligent actio

ReasonSTL: Bridging Natural Language and Signal Temporal Logic via Tool-Augmented Process-Rewarded Learning

arXiv:2605.06483v2 Announce Type: replace Abstract: Signal Temporal Logic (STL) is an expressive formal language for specifying spatio-temporal requirements over real-valued, real-time signals. It has

Rebalancing gradient to improve self-supervised co-training of depth, odometry and optical flow predictions

arXiv:2605.07945v1 Announce Type: new Abstract: We present CoopNet, an approach that improves the cooperation of co-trained networks by dynamically adapting the apportionment of gradient, to ensure eq

Agents

RelAgent: LLM Agents as Data Scientists for Relational Learning

arXiv:2605.07840v1 Announce Type: new Abstract: Relational learning is a challenging problem that has motivated a wide range of approaches, including graph-based models (e.g., graph neural networks, g

agentsarxiv-cs-lg

RELO: Reinforcement Learning to Localize for Visual Object Tracking

arXiv:2605.07379v1 Announce Type: cross Abstract: Conventional visual object trackers localize targets using handcrafted spatial priors, often in the form of heatmaps. Such priors provide only surroga

Rethinking Importance Sampling in LLM Policy Optimization: A Cumulative Token Perspective

arXiv:2605.07331v1 Announce Type: cross Abstract: Reinforcement learning, including reinforcement learning with verifiable rewards (RLVR), has emerged as a powerful approach for LLM post-training. Cen

Rethinking Weight Tying: Pseudo-Inverse Tying for LM Stable Training and Updates

model-releasesarxiv-cs-cl

arXiv:2602.04556v2 Announce Type: replace Abstract: Weight tying is widely used in compact language models to reduce parameters by sharing the token table between the input embedding and the output pr

Revisiting Transformer Layer Parameterization Through Causal Energy Minimization

arXiv:2605.07588v1 Announce Type: cross Abstract: Transformer blocks typically combine multi-head attention (MHA) for token mixing with gated MLPs for token-wise feature transformation, yet many choic

RIDER: 3D RNA Inverse Design with Reinforcement Learning-Guided Diffusion

arXiv:2602.16548v2 Announce Type: replace Abstract: The inverse design of RNA three-dimensional (3D) structures is crucial for engineering functional RNAs in synthetic biology and therapeutics. While

RL-RIG: A Generative Spatial Reasoner via Intrinsic Reflection

arXiv:2602.19974v2 Announce Type: replace Abstract: Recent advancements in image generation have achieved impressive results in producing high-quality images. However, existing image generation models

Applications

Robust and Reliable AI for Predictive Quality in Semiconductor Materials Manufacturing with MLOps and Uncertainty Quantification

arXiv:2605.07752v1 Announce Type: new Abstract: Semiconductor materials manufacturing presents unique challenges for machine learning deployment due to evolving process conditions, equipment degradati

applicationsarxiv-cs-lg

Rubric-Grounded RL: Structured Judge Rewards for Generalizable Reasoning

arXiv:2605.08061v1 Announce Type: new Abstract: We argue that decomposing reward into weighted, verifiable criteria and using an LLM judge to score them provides a partial-credit optimization signal:

S2M-Net: Spectral-Spatial Mixing for Medical Image Segmentation with Morphology-Aware Adaptive Loss

arXiv:2601.01285v2 Announce Type: replace Abstract: Medical image segmentation requires balancing local precision for boundary-critical clinical applications, global context for anatomical coherence,

Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence

arXiv:2605.06230v2 Announce Type: replace Abstract: As large models evolve from conversational assistants into autonomous agents, challenges increasingly arise from long-horizon decision making, tool

Tutorials

Same Brain, Different Prediction: How Preprocessing Choices Undermine EEG Decoding Reliability

arXiv:2605.07212v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a cornerstone of brain-computer interfaces and clinical neuroscience, yet deep learning models are typically trained a

tutorialsarxiv-cs-ai

Scaling Categorical Flow Maps

model-releasesarxiv-cs-lg

arXiv:2605.07820v1 Announce Type: new Abstract: Continuous diffusion and flow matching models could represent a powerful alternative to autoregressive approaches for language modelling (LM), as they u

Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts

arXiv:2602.03473v2 Announce Type: replace-cross Abstract: Continual learning, especially class-incremental learning (CIL), on the basis of a pre-trained model (PTM) has garnered substantial research i

Agents

SCOUT: Closed-Loop in-vivo System for Continuous Methane Concentration Monitoring in Cattle

arXiv:2508.04056v2 Announce Type: replace Abstract: Enteric methane measurement from ruminant livestock faces fundamental trade-offs between accuracy and operational feasibility. Existing methods quan

agentsarxiv-cs-ro

Local Ai

Seeing Across Skies and Streets: Feedforward 3D Reconstruction from Satellite, Drone, and Ground Images

arXiv:2605.07978v1 Announce Type: new Abstract: Cross-view localization classically asks: where does this ground image lie on the satellite tile? Existing methods are typically limited to 3-DoF estima

local-aiarxiv-cs-cv

Self-Consolidating Language Models: Continual Knowledge Incorporation from Context

arXiv:2605.07076v1 Announce Type: new Abstract: Large language models (LLMs) increasingly receive information as streams of passages, conversations, and long-context workflows. While longer context wi

researcharxiv-cs-cl

Semiparametric Efficient Test for Interpretable Distributional Treatment Effects

arXiv:2605.08034v1 Announce Type: cross Abstract: Distributional treatment effects can be invisible to means: a treatment may preserve average outcomes while changing tails, modes, dispersion, or rare

researcharxiv-cs-lg

SEQUOR: A Multi-Turn Benchmark for Realistic Constraint Following

model-releasesarxiv-cs-cl

arXiv:2605.06353v2 Announce Type: replace Abstract: In a conversation, a helpful assistant must reliably follow user directives, even as they refine, modify, or contradict earlier requests. Yet most i

Setting-Matched and Semantics-Scaled Benchmarking of One-Step Generative Models Against Multistep Diffusion and Flow Models

arXiv:2603.14186v4 Announce Type: replace Abstract: State-of-the-art text-to-image models produce high-quality images, but inference remains expensive as generation requires several sequential ODE or

SHARP: A Self-Evolving Human-Auditable Rubric Policy for Financial Trading Agents

arXiv:2605.06822v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed for autonomous financial trading, a domain requiring continuous adaptation to noisy, non-stationa

SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion

arXiv:2605.07482v1 Announce Type: cross Abstract: Machine unlearning for large language models (LLMs) aims to selectively remove memorized content such as private data, copyrighted text, or hazardous

researcharxiv-cs-ai

Local Ai

Signal Reshaping for GRPO in Weak-Feedback Agentic Code Repair

arXiv:2605.07276v1 Announce Type: new Abstract: Code-agent RL often receives weak feedback: rollout-time signals are reliable and executable, but capture only necessary or surface conditions for task

local-aiarxiv-cs-ai

Skip-It? Theoretical Conditions for Layer Skipping in Vision-Language Models

arXiv:2509.25584v2 Announce Type: replace Abstract: Vision-language models achieve incredible performance across a wide range of tasks, but their large size makes inference costly. Recent work has sho

researcharxiv-cs-ai

SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks

arXiv:2603.24755v2 Announce Type: replace-cross Abstract: Software development is iterative, yet agentic coding benchmarks hide design issues through their single-shot setup. Recent iterative benchmar

Tutorials

SLOPE: Optimistic Potential Landscape Shaping for Model-based Reinforcement Learning

arXiv:2602.03201v3 Announce Type: replace Abstract: Model-based reinforcement learning (MBRL) is sample-efficient but struggles in sparse reward settings. A critical bottleneck arises from the lack of

tutorialsarxiv-cs-lg

SMT-Based Active Learning of Weighted Automata

arXiv:2605.07758v1 Announce Type: cross Abstract: We present an SMT-based active learning algorithm for nondeterministic weighted automata (WFAs) as a practical and robust alternative to Hankel/L*-sty

researcharxiv-cs-lg

SREGym: A Live Benchmark for AI SRE Agents with High-Fidelity Failure Scenarios

arXiv:2605.07161v1 Announce Type: new Abstract: AI agents are increasingly used to diagnose and mitigate failures in production systems, known as agentic Site Reliability Engineering (SRE). Current SR

Stationary Reweighting Yields Local Convergence of Soft Fitted Q-Iteration

arXiv:2512.23927v2 Announce Type: replace-cross Abstract: Fitted Q-iteration (FQI) and soft FQI are widely used value-based methods for offline reinforcement learning, but their standard stability gua

Agents

Switchcraft: AI Model Router for Agentic Tool Calling

arXiv:2605.07112v1 Announce Type: new Abstract: Agentic AI systems that invoke external tools are powerful but costly, leading developers to default to large models and overspend inference budgets. Mo

agentsarxiv-cs-ai

Applications

Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference

arXiv:2605.07639v1 Announce Type: new Abstract: Tacit knowledge plays a central role in human expertise, yet it remains difficult to capture, formalize, and reuse in machine-interpretable form. This c

applicationsarxiv-cs-ai

TAG-K: Tail-Averaged Greedy Kaczmarz for Computationally Efficient and Performant Online Inertial Parameter Estimation

model-releasesarxiv-cs-ro

arXiv:2510.04839v2 Announce Type: replace Abstract: Accurate online inertial parameter estimation is essential for adaptive robotic control, enabling real-time adjustment to payload changes, environme

TajPersLexon: A Tajik-Persian Lexical Resource and Hybrid Model for Cross-Script Low-Resource NLP

model-releasesarxiv-cs-cl

arXiv:2605.06886v1 Announce Type: new Abstract: This work introduces TajPersLexon, a curated Tajik--Persian parallel lexical resource of 40,112 word and short-phrase pairs for cross-script lexical ret

Task-Oriented Communication for Human Action Understanding via Edge-Cloud Co-Inference

arXiv:2605.07354v1 Announce Type: cross Abstract: The expanding application of smart sensing has created a growing demand for the accurate understanding of human action at the network edge. Traditiona