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SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learning

model-releasesarxiv-cs-lg

arXiv:2604.24729v1 Announce Type: new Abstract: Specification-guided reinforcement learning (RL) provides a principled framework for encoding complex, temporally extended tasks using formal specificat

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Human DGX agent

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Spectro-Temporal Modulation Representation Framework for Human-Imitated Speech Detection

DGX agent

arXiv:2604.23241v1 Announce Type: cross Abstract: Human-imitated speech poses a greater challenge than AI-generated speech for both human listeners and automatic detection systems. Unlike AI-generated

researcharxiv-cs-cl

28 Apr 2026

Speech Enhancement Based on Drifting Models

arXiv:2604.24199v1 Announce Type: cross Abstract: We propose Speech Enhancement based on Drifting Models (DriftSE), a novel generative framework that formulates denoising as an equilibrium problem. Ra

Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization

arXiv:2502.12672v4 Announce Type: replace-cross Abstract: Fine-tuning speech representation models can enhance performance on specific tasks but often compromises their cross-task generalization abili

Sphere-Depth: A Benchmark for Depth Estimation Methods with Varying Spherical Camera Orientations

arXiv:2604.23432v1 Announce Type: cross Abstract: Reliable depth estimation from spherical images is crucial for 360{eg} vision in robotic navigation and immersive scene understanding. However, the on

SPLIT: Separating Physical-Contact via Latent Arithmetic in Image-Based Tactile Sensors

arXiv:2604.24449v1 Announce Type: cross Abstract: Training machine learning models for robotic tactile sensing requires vast amounts of data, yet obtaining realistic interaction data remains a challen

SRL-CLIP: Efficient CLIP Video Adaptation via Structured Semantic Role Labels

arXiv:2401.07669v2 Announce Type: replace Abstract: Adapting CLIP for videos has gained popularity due to its semantic and rich representation. While CLIP is a good starting point, it typically underg

tutorialsarxiv-cs-cv

SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation

arXiv:2505.16637v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently demonstrated remarkable capabilities in machine translation (MT). However, most advanced MT-specifi

Stabilizing Efficient Reasoning with Step-Level Advantage Selection

arXiv:2604.24003v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong reasoning performance by allocating substantial computation at inference time, often generating long and ver

StackFeat RL: Reinforcement Learning over Iterative Dual Criterion Feature Selection for Stable Biomarker Discovery

arXiv:2604.22892v1 Announce Type: new Abstract: Feature selection in high-dimensional genomic data (d gg n) demands methods that are simultaneously accurate, sparse, and stable. Existing approaches ei

safetyarxiv-cs-lg

STAND: Semantic Anchoring Constraint with Dual-Granularity Disambiguation for Remote Sensing Image Change Captioning

arXiv:2604.23309v1 Announce Type: new Abstract: Remote sensing image change captioning (RSICC) aims to describe the difference between two remote sensing images. While recent methods have explored vid

researcharxiv-cs-cv

Local Ai

Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference

arXiv:2402.11789v5 Announce Type: replace-cross Abstract: Anomaly localization in images -- identifying regions that deviate from normal patterns -- is vital in applications such as medical diagnosis

local-aiarxiv-cs-cv

Applications

Statistically-Guided Meta-Learning for Cross-Deployment Activity Recognition in Distributed Fiber-Optic Sensing

arXiv:2511.17902v3 Announce Type: replace-cross Abstract: Distributed Fiber Optic Sensing (DFOS) is promising for long-range perimeter security, yet practical deployment faces three key obstacles: sev

applicationsarxiv-cs-ai

STELLAR-E: a Synthetic, Tailored, End-to-end LLM Application Rigorous Evaluator

arXiv:2604.24544v1 Announce Type: new Abstract: The increasing reliance on Large Language Models (LLMs) across diverse sectors highlights the need for robust domain-specific and language-specific eval

StereoSpace: Depth-Free Synthesis of Stereo Geometry via End-to-End Diffusion in a Canonical Space

arXiv:2512.10959v2 Announce Type: replace Abstract: We introduce StereoSpace, a diffusion-based framework for monocular-to-stereo synthesis that models geometry purely through viewpoint conditioning,

tutorialsarxiv-cs-cv

Stochastic KV Routing: Enabling Adaptive Depth-Wise Cache Sharing

arXiv:2604.22782v1 Announce Type: cross Abstract: Serving transformer language models with high throughput requires caching Key-Values (KVs) to avoid redundant computation during autoregressive genera

Stochastic simultaneous optimistic optimization

arXiv:2604.24537v1 Announce Type: new Abstract: We study the problem of global maximization of a function f given a finite number of evaluations perturbed by noise. We consider a very weak assumption

StoryTR: Narrative-Centric Video Temporal Retrieval with Theory of Mind Reasoning

arXiv:2604.23198v1 Announce Type: new Abstract: Current video moment retrieval excels at action-centric tasks but struggles with narrative content. Models can see extit{what is happening} but fail to

Strategic Bidding in 6G Spectrum Auctions with Large Language Models

arXiv:2604.24156v1 Announce Type: cross Abstract: Efficient and fair spectrum allocation is a central challenge in 6G networks, where massive connectivity and heterogeneous services continuously compe

StratRAG: A Multi-Hop Retrieval Evaluation Dataset for Retrieval-Augmented Generation Systems

arXiv:2604.22757v1 Announce Type: cross Abstract: We introduce StratRAG, an open-source retrieval evaluation dataset for benchmarking Retrieval-Augmented Generation (RAG) systems on multi-hop reasonin

Stress-Testing Emotional Support Models: Moving from Homogeneous to Diverse Help Seekers

arXiv:2601.07698v2 Announce Type: replace Abstract: As emotional support chatbots have recently gained significant traction across both research and industry, a common evaluation strategy has emerged:

StructAlign: Structured Cross-Modal Alignment for Continual Text-to-Video Retrieval

model-releasesarxiv-cs-cv

arXiv:2601.20597v2 Announce Type: replace Abstract: Continual Text-to-Video Retrieval (CTVR) is a challenging multimodal continual learning setting, where models must incrementally learn new semantic

Structural Enforcement of Goal Integrity in AI Agents via Separation-of-Powers Architecture

arXiv:2604.23646v1 Announce Type: new Abstract: Recent evidence suggests that frontier AI systems can exhibit agentic misalignment, generating and executing harmful actions derived from internally con

safetyarxiv-cs-ai

Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency

arXiv:2604.24380v1 Announce Type: new Abstract: While Large Vision Language Models (LVLMs) demonstrate impressive capabilities, their substantial computational and memory requirements pose deployment

Structure Guided Retrieval-Augmented Generation for Factual Queries

arXiv:2604.22843v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has been proposed to mitigate hallucinations in large language models (LLMs), where generated outputs may be fact

tutorialsarxiv-cs-ai

Supernodes and Halos: Loss-Critical Hubs in LLM Feed-Forward Layers

arXiv:2604.23475v1 Announce Type: cross Abstract: We study the organization of channel-level importance in transformer feed-forward networks (FFNs). Using a Fisher-style loss proxy (LP) based on activ

Supporting Family-School Partnerships with Robot-Facilitated Home-Based Activities

arXiv:2604.23978v1 Announce Type: new Abstract: Family-school partnerships (FSP) are critical to children's development, yet families often face barriers such as time constraints, fragmented communica

researcharxiv-cs-ro

Surface Sensitivity in Lean 4 Autoformalization

arXiv:2604.23135v1 Announce Type: new Abstract: Natural-language variation poses a key challenge in Lean autoformalization: semantically equivalent paraphrases of the same theorem statements can induc

Survey in Characterizing Semantic Change

arXiv:2402.19088v5 Announce Type: replace-cross Abstract: Live languages continuously evolve to integrate the cultural change of human societies. This evolution manifests through neologisms (new words

tutorialsarxiv-cs-ai

Swa-bhasha Resource Hub: Romanized Sinhala to Sinhala Transliteration Systems and Data Resources

arXiv:2507.09245v2 Announce Type: replace Abstract: The Swa-bhasha Resource Hub provides a comprehensive collection of data resources and algorithms developed for Romanized Sinhala to Sinhala translit

researcharxiv-cs-cl

Agents

SwarmDrive: Semantic V2V Coordination for Latency-Constrained Cooperative Autonomous Driving

arXiv:2604.22852v1 Announce Type: cross Abstract: Cloud-hosted LLM inference for autonomous driving adds round-trip delay and depends on stable connectivity, while purely local edge models struggle un

agentsarxiv-cs-ai

Agents

SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents

arXiv:2601.16746v3 Announce Type: replace-cross Abstract: LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts,

agentsarxiv-cs-cl

SWE-QA: Can Language Models Answer Repository-level Code Questions?

arXiv:2509.14635v2 Announce Type: replace Abstract: Understanding and reasoning about entire software repositories is an essential capability for intelligent software engineering tools. While existing

SwissGov-RSD: A Human-annotated, Cross-lingual Benchmark for Token-level Recognition of Semantic Differences Between Related Documents

arXiv:2512.07538v3 Announce Type: replace Abstract: Recognizing semantic differences across documents is crucial for text generation evaluation and content alignment, especially in cross-lingual setti

Switch Attention: Towards Dynamic and Fine-grained Hybrid Transformers

arXiv:2603.26380v2 Announce Type: replace Abstract: The attention mechanism has been the core component in modern transformer architectures. However, the computation of standard full attention scales

SycoPhantasy: Quantifying Sycophancy and Hallucination in Small Open Weight VLMs for Vision-Language Scoring of Fantasy Characters

arXiv:2604.24346v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed as evaluators in tasks requiring nuanced image understanding, yet their reliability in scoring

Symbolic recovery of PDEs from measurement data

arXiv:2602.15603v2 Announce Type: replace Abstract: Models based on partial differential equations (PDEs) are powerful for describing a wide range of complex phenomena in the natural sciences. Accurat

Symmetric Equilibrium Propagation for Thermodynamic Diffusion Training

arXiv:2604.23806v1 Announce Type: cross Abstract: The reverse process in score-based diffusion models is formally equivalent to overdamped Langevin dynamics in a time-dependent energy landscape. In ou

Synthetic Eggs in Many Baskets: The Impact of Synthetic Data Diversity on LLM Fine-Tuning

arXiv:2511.01490v2 Announce Type: replace Abstract: As synthetic data becomes widely used in language model development, understanding its impact on model behavior is crucial. This paper investigates

safetyarxiv-cs-cl

SynthPert: Enhancing LLM Biological Reasoning via Synthetic Reasoning Traces for Cellular Perturbation Prediction

arXiv:2509.25346v2 Announce Type: replace Abstract: Predicting cellular responses to genetic perturbations represents a fundamental challenge in systems biology, critical for advancing therapeutic dis

TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training

arXiv:2604.24088v1 Announce Type: cross Abstract: Handling communication overhead in large-scale tensor-parallel training remains a critical challenge due to the dense, near-zero distributions of inte

Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling

arXiv:2604.23586v1 Announce Type: cross Abstract: Joint audio-video generation models have shown that unified generation yields stronger cross-modal coherence than cascaded approaches. However, existi

researcharxiv-cs-cl

Applications

Talking Slide Avatars: Open-Source Multimodal Communication Approach for Teaching

arXiv:2604.23703v1 Announce Type: cross Abstract: Slide-based teaching is widely used in higher education, yet in online, hybrid, and asynchronous contexts, slides often lose the instructor presence,

applicationsarxiv-cs-ai

Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning

arXiv:2604.23623v1 Announce Type: new Abstract: Recent advancements in large language models (LLMs) have catalyzed the rise of reasoning-intensive inference paradigms, where models perform explicit st

tutorialsarxiv-cs-ai

Task-guided Spatiotemporal Network with Diffusion Augmentation for EEG-based Dementia Diagnosis and MMSE Prediction

arXiv:2604.23964v1 Announce Type: cross Abstract: Patients with dementia typically exhibit cognitive impairment, which is routinely assessed using the Mini-Mental State Examination (MMSE). Concurrentl

TCOD: Exploring Temporal Curriculum in On-Policy Distillation for Multi-turn Autonomous Agents

arXiv:2604.24005v1 Announce Type: cross Abstract: On-policy distillation (OPD) has shown strong potential for transferring reasoning ability from frontier or domain-specific models to smaller students

safetyarxiv-cs-ai

Agents

TeachMaster: Generative Teaching via Code

arXiv:2601.04204v2 Announce Type: replace-cross Abstract: The scalability of high-quality online education is hindered by the high costs and slow cycles of manual content creation. Despite advancement

agentsarxiv-cs-ai

TEMPO: Transformers for Temporal Disease Progression from Cross-Sectional Data

arXiv:2604.23368v1 Announce Type: new Abstract: Event-Based Models (EBMs) infer biomarker progression from cross-sectional data but typically only as ordinal sequences and rely on rigid model assumpti