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StepCodeReasoner: Aligning Code Reasoning with Stepwise Execution Traces via Reinforcement Learning

model-releasesarxiv-cs-cl

arXiv:2605.11922v1 Announce Type: cross Abstract: Existing code reasoning methods primarily supervise final code outputs, ignoring intermediate states, often leading to reward hacking where correct an

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Stochastic Minimum-Cost Reach-Avoid Reinforcement Learning

DGX agent

arXiv:2605.11975v1 Announce Type: new Abstract: We study stochastic minimum-cost reach-avoid reinforcement learning, where an agent must satisfy a reach-avoid specification with probability at least p

agentsarxiv-cs-lg

13 May 2026

StoicLLM: Preference Optimization for Philosophical Alignment in Small Language Models

model-releasesarxiv-cs-cl

arXiv:2605.11483v1 Announce Type: new Abstract: While large language models excel at factual adaptation, their ability to internalize nuanced philosophical frameworks under severe data constraints rem

Stop Marginalizing My Dreams: Model Inversion via Laplace Kernel for Continual Learning

arXiv:2605.11804v1 Announce Type: cross Abstract: Data-free continual learning (DFCIL) relies on model inversion to synthesize pseudo-samples and mitigate catastrophic forgetting. However, existing in

Stopping Computation for Converged Tokens in Masked Diffusion-LM Decoding

arXiv:2602.06412v3 Announce Type: replace Abstract: Masked Diffusion Language Models generate sequences via iterative sampling that progressively unmasks tokens. However, they still recompute the atte

Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space

arXiv:2605.12412v1 Announce Type: new Abstract: Large Language Models (LLMs) update their behavior in context, which can be viewed as a form of Bayesian inference. However, the structure of the latent

STRABLE: Benchmarking Tabular Machine Learning with Strings

arXiv:2605.12292v1 Announce Type: new Abstract: Benchmarking tabular learning has revealed the benefit of dedicated architectures, pushing the state of the art. But real-world tables often contain str

applicationsarxiv-cs-lg

Tutorials

Strategically Deceptive Model Deployment in Performative Prediction

arXiv:2506.09044v2 Announce Type: replace Abstract: Machine Learning systems are increasingly deployed in decision-making settings that shape user behavior and, in turn, the data on which future decis

tutorialsarxiv-cs-lg

Streaming of rendered content with adaptive frame rate and resolution

arXiv:2605.10995v1 Announce Type: cross Abstract: Streaming rendered content is an attractive way to bring high-quality graphics to billions of mobile devices that do not have sufficient rendering pow

STRIDE: Training-Free Diversity Guidance via PCA-Directed Feature Perturbation in Single-Step Diffusion Models

arXiv:2605.11494v1 Announce Type: new Abstract: Distilled one-step (T=1) or few-step (Tleq4) diffusion models enable real-time image generation but often exhibit reduced sample diversity compared to t

safetyarxiv-cs-cv

Local Ai

Structural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning

arXiv:2605.10985v1 Announce Type: new Abstract: Protein language models such as ESM-2 learn rich residue representations that achieve strong performance on protein function prediction, but their featu

local-aiarxiv-cs-lg

STRUM: A Spectral Transcription and Rhythm Understanding Model for End-to-End Generation of Playable Rhythm-Game Charts

arXiv:2605.12135v1 Announce Type: cross Abstract: We present STRUM (Spectral Transcription and Rhythm Understanding Model), an audio-to-chart pipeline that converts raw recordings into playable Clone

Support-Proximity Augmented Diffusion Estimation for Offline Black-Box Optimization

arXiv:2605.11246v1 Announce Type: new Abstract: Offline black-box optimization aims to discover novel designs with high property scores using only a static dataset, a task fundamentally challenged by

SURGE: Surrogate Gradient Adaptation in Binary Neural Networks

arXiv:2605.10989v1 Announce Type: new Abstract: The training of Binary Neural Networks (BNNs) is fundamentally based on gradient approximation for non-differentiable binarization operations (e.g., sig

SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis

arXiv:2511.11935v2 Announce Type: replace Abstract: Deep-learning survival models for electronic health record (EHR) data are hard to compare across papers because the upstream preprocessing step, whi

SyncDPO: Enhancing Temporal Synchronization in Video-Audio Joint Generation via Preference Learning

model-releasesarxiv-cs-cv

arXiv:2605.12179v1 Announce Type: new Abstract: Recent advancements in video-audio joint generation have achieved remarkable success in semantic correspondence. However, achieving precise temporal syn

Tutorials

Synthetic Function Demonstrations Improve Generation in Low-Resource Programming Languages

arXiv:2503.18760v2 Announce Type: replace Abstract: A key consideration when training an LLM is whether the target language is more or less resourced, for example English compared to Welsh, or Python

tutorialsarxiv-cs-cl

TabDLM: Free-Form Tabular Data Generation via Joint Numerical-Language Diffusion

arXiv:2602.22586v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation has attracted growing attention due to its importance for data augmentation, foundation models, and privacy.

applicationsarxiv-cs-cl

Tackling Fake Forgetting through Uncertainty Quantification

arXiv:2501.19403v3 Announce Type: replace Abstract: Machine unlearning seeks to remove the influence of specified data from a trained model. While the unlearning accuracy provides a widely used metric

Tacmap: Bridging the Tactile Sim-to-Real Gap via Geometry-Consistent Penetration Depth Map

arXiv:2602.21625v2 Announce Type: replace Abstract: Vision-Based Tactile Sensors (VBTS) are essential for achieving dexterous robotic manipulation, yet the tactile sim-to-real gap remains a fundamenta

safetyarxiv-cs-ro

Taking the Road Less Scheduled with Adaptive Polyak Steps

arXiv:2511.07767v2 Announce Type: replace Abstract: Schedule-Free SGD, proposed in [Defazio et al., 2024], achieves optimal convergence rates without requiring the training horizon in advance, by repl

Taming Extreme Tokens: Covariance-Aware GRPO with Gaussian-Kernel Advantage Reweighting

arXiv:2605.11538v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has emerged as a promising approach for improving the reasoning capabilities of large language models. However

safetyarxiv-cs-cl

Taming Score-Based Denoisers in ADMM: A Convergent Plug-and-Play Framework

arXiv:2603.10281v3 Announce Type: replace-cross Abstract: While score-based generative models have emerged as powerful priors for solving inverse problems, directly integrating them into optimization

TAR: Text Semantic Assisted Cross-modal Image Registration Framework for Optical and SAR Images

arXiv:2605.12064v1 Announce Type: new Abstract: Existing deep learning-based methods can capture shared features from optical and synthetic aperture radar (SAR) images for spatial alignment. However,

safetyarxiv-cs-cv

Targeted Neuron Modulation via Contrastive Pair Search

arXiv:2605.12290v1 Announce Type: new Abstract: Language models are instruction-tuned to refuse harmful requests, but the mechanisms underlying this behavior remain poorly understood. Popular steering

Targeted Tests for LLM Reasoning: An Audit-Constrained Protocol

arXiv:2605.11599v1 Announce Type: new Abstract: Fixed reasoning benchmarks evaluate canonical prompts, but semantically valid changes in presentation can still change model behavior. Studies of prompt

Task-Adaptive Embedding Refinement via Test-time LLM Guidance

arXiv:2605.12487v1 Announce Type: new Abstract: We explore the effectiveness of an LLM-guided query refinement paradigm for extending the usability of embedding models to challenging zero-shot search

TB-AVA: Text as a Semantic Bridge for Audio-Visual Parameter Efficient Finetuning

model-releasesarxiv-cs-cv

arXiv:2605.11572v1 Announce Type: new Abstract: Audio-visual understanding requires effective alignment between heterogeneous modalities, yet cross-modal correspondence remains challenging when tempor

TCP-SSM: Efficient Vision State Space Models with Token-Conditioned Poles

arXiv:2605.11563v1 Announce Type: new Abstract: State Space Models (SSMs) have emerged as a compelling alternative to attention models for long-range vision tasks, offering input-dependent recurrence

Test-Time Compute for Dense Retrieval: Agentic Program Generation with Frozen Embedding Models

model-releasesarxiv-cs-cl

arXiv:2605.11374v1 Announce Type: cross Abstract: Test-time compute is widely believed to benefit only large reasoning models. We show it also helps small embedding models. Most modern embedding check

Test-Time Personalization: A Diagnostic Framework and Probabilistic Fix for Scaling Failures

arXiv:2605.10991v1 Announce Type: new Abstract: Existing approaches to LLM personalization focus on constructing better personalized models or inputs, while treating inference as a single-shot process

Testing General Relativity Through Gravitational Wave Classification: A Convolutional Neural Network Framework

arXiv:2605.02453v1 Announce Type: cross Abstract: We present a machine learning framework for testing general relativity (GR) with gravitational wave signals from binary black hole mergers. Using the

Local Ai

TextSeal: A Localized LLM Watermark for Provenance & Distillation Protection

arXiv:2605.12456v1 Announce Type: cross Abstract: We introduce TextSeal, a state-of-the-art watermark for large language models. Building on Gumbel-max sampling, TextSeal introduces dual-key generatio

local-aiarxiv-cs-cl

The Algorithmic Caricature: Auditing LLM-Generated Political Discourse Across Crisis Events

arXiv:2605.12452v1 Announce Type: new Abstract: Large Language Models (LLMs) can generate fluent political text at scale, raising concerns about synthetic discourse during crises and social conflict.

The Bicameral Model: Bidirectional Hidden-State Coupling Between Parallel Language Models

arXiv:2605.11167v1 Announce Type: new Abstract: Existing multi-model and tool-augmented systems communicate by generating text, serializing every exchange through the output vocabulary. Can two pretra

The Challenge and Reward of Fair Play in Narrative: A Computational Approach

arXiv:2507.13841v2 Announce Type: replace Abstract: Good storytelling involves surprise -- unpredictability in how the story unfolds -- and sense-making, the requirement that the story forms a coheren

Tutorials

The Confusion is Real: GRAPHIC -- A Network Science Approach to Confusion Matrices in Deep Learning

arXiv:2602.19770v2 Announce Type: replace Abstract: Explainable artificial intelligence has emerged as a promising field of research to address reliability concerns in artificial intelligence. Despite

tutorialsarxiv-cs-lg

The DAWN of World-Action Interactive Models

arXiv:2605.11550v1 Announce Type: new Abstract: A plausible scene evolution depends on the maneuver being considered, while a good maneuver depends on how the scene may evolve. Existing World Action M

safetyarxiv-cs-cv

The Evaluation Differential: When Frontier AI Models Recognise They Are Being Tested

arXiv:2605.11496v1 Announce Type: cross Abstract: Recent published evidence from frontier laboratories shows that contemporary AI models can recognise evaluation contexts, latently represent them, and

The first global agricultural field boundary map at 10m resolution

arXiv:2605.11055v1 Announce Type: new Abstract: The agricultural field is the natural unit at which crops are planted, managed, regulated, and reported, yet most global remote-sensing products for agr

Hardware

The Illusion of Power Capping in LLM Decode: A Phase-Aware Energy Characterisation Across Attention Architectures

arXiv:2605.11999v1 Announce Type: cross Abstract: Power capping is the standard GPU energy lever in LLM serving, and it appears to work: throughput drops, power readings fall, and energy budgets are m

hardwarearxiv-cs-lg

The Luna Bound Propagator for Formal Analysis of Neural Networks

arXiv:2603.23878v2 Announce Type: replace Abstract: The parameterized CROWN analysis, a.k.a., alpha-CROWN has emerged as a practically successful abstract interpretation method for neural network veri

applicationsarxiv-cs-lg

The Midas Touch for Metric Depth

arXiv:2605.11578v1 Announce Type: new Abstract: Recent advances have markedly improved the cross-scene generalization of relative depth estimation, yet its practical applicability remains limited by t

The Missing GAP: From Solving Square Jigsaw Puzzles to Handling Real World Archaeological Fragments

arXiv:2605.12077v1 Announce Type: new Abstract: Jigsaw puzzle solving has been an increasingly popular task in the computer vision research community. Recent works have utilized cutting-edge architect

applicationsarxiv-cs-cv

The Offline-Frontier Shift: Diagnosing Distributional Limits in Generative Multi-Objective Optimization

arXiv:2602.11126v2 Announce Type: replace Abstract: Offline multi-objective optimization (MOO) aims to recover Pareto-optimal designs given a finite, static dataset. Recent generative approaches, incl

The Price of Proportional Representation in Temporal Voting

arXiv:2605.11157v1 Announce Type: cross Abstract: We study proportional representation in the temporal voting model, where collective decisions are made repeatedly over time over a fixed horizon. Prio

The Scaling Law of Evaluation Failure: Why Simple Averaging Collapses Under Data Sparsity and Item Difficulty Gaps, and How Item Response Theory Recovers Ground Truth Across Domains

arXiv:2605.11205v1 Announce Type: new Abstract: Benchmark evaluation across AI and safety-critical domains overwhelmingly relies on simple averaging. We demonstrate that this practice produces substan

The tractability landscape of diffusion alignment: regularization, rewards, and computational primitives

arXiv:2605.11361v1 Announce Type: new Abstract: Inference-time reward alignment asks how to turn a pre-trained diffusion model with base law p into a sampler that favors a reward r while remaining clo