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Siamese Foundation Models for Crystal Structure Prediction

arXiv:2503.10471v2 Announce Type: replace-cross Abstract: Predicting crystal structures from chemical compositions is a fundamental challenge in materials discovery, complicated by complex 3D geometri

tutorialsarxiv-cs-ai

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Silo-Bench: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems

DGX agent

arXiv:2603.01045v2 Announce Type: replace-cross Abstract: Large language models are increasingly deployed in multi-agent systems to overcome context limitations by distributing information across agen

model-releasesarxiv-cs-ai

15 Apr 2026

Simulation as Supervision: Mechanistic Pretraining for Scientific Discovery

arXiv:2507.08977v4 Announce Type: replace-cross Abstract: Scientific modeling faces a tradeoff between the interpretability of mechanistic theory and the predictive power of machine learning. While ex

SinkSAM-Net: Knowledge-Driven Self-Supervised Sinkhole Segmentation Using Topographic Priors and Segment Anything Model

arXiv:2410.01473v2 Announce Type: replace Abstract: Soil sinkholes significantly influence soil degradation, infrastructure vulnerability, and landscape evolution. However, their irregular shapes, com

SIR-Bench: Evaluating Investigation Depth in Security Incident Response Agents

arXiv:2604.12040v1 Announce Type: cross Abstract: We present SIR-Bench, a benchmark of 794 test cases for evaluating autonomous security incident response agents that distinguishes genuine forensic in

SIRI-Bench: Challenging VLMs' Spatial Intelligence through Complex Reasoning Tasks

arXiv:2506.14512v4 Announce Type: replace Abstract: Large Language Models (LLMs) have undergone rapid progress, largely attributed to reinforcement learning on complex reasoning tasks. In contrast, wh

Skill-informed Data-driven Haptic Nudges for High-dimensional Human Motor Learning

arXiv:2603.12583v2 Announce Type: replace Abstract: In this work, we propose a data-driven framework to design optimal haptic nudge feedback leveraging the learner's estimated skill to address the cha

safetyarxiv-cs-ro

Applications

SmellNet: A Large-scale Dataset for Real-world Smell Recognition

arXiv:2506.00239v5 Announce Type: replace Abstract: The ability of AI to sense and identify various substances based on their smell alone can have profound impacts on allergen detection (e.g. smelling

applicationsarxiv-cs-ai

SOAR: Self-Correction for Optimal Alignment and Refinement in Diffusion Models

arXiv:2604.12617v1 Announce Type: cross Abstract: The post-training pipeline for diffusion models currently has two stages: supervised fine-tuning (SFT) on curated data and reinforcement learning (RL)

Social Learning Strategies for Evolved Virtual Soft Robots

arXiv:2604.12482v1 Announce Type: cross Abstract: Optimizing the body and brain of a robot is a coupled challenge: the morphology determines what control strategies are effective, while the control pa

tutorialsarxiv-cs-ai

Socrates Loss: Unifying Confidence Calibration and Classification by Leveraging the Unknown

arXiv:2604.12245v1 Announce Type: cross Abstract: Deep neural networks, despite their high accuracy, often exhibit poor confidence calibration, limiting their reliability in high-stakes applications.

SOLARIS: Speculative Offloading of Latent-bAsed Representation for Inference Scaling

arXiv:2604.12110v1 Announce Type: new Abstract: Recent advances in recommendation scaling laws have led to foundation models of unprecedented complexity. While these models offer superior performance,

SpanKey: Dynamic Key Space Conditioning for Neural Network Access Control

arXiv:2604.12254v1 Announce Type: cross Abstract: SpanKey is a lightweight way to gate inference without encrypting weights or chasing leaderboard accuracy on gated inference. The idea is to condition

Sparse Growing Transformer: Training-Time Sparse Depth Allocation via Progressive Attention Looping

model-releasesarxiv-cs-cl

arXiv:2603.23998v2 Announce Type: replace Abstract: Existing approaches to increasing the effective depth of Transformers predominantly rely on parameter reuse, extending computation through recursive

SparseWorld-TC: Trajectory-Conditioned Sparse Occupancy World Model

arXiv:2511.22039v3 Announce Type: replace Abstract: This paper introduces a novel architecture for trajectory-conditioned forecasting of future 3D scene occupancy. In contrast to methods that rely on

Spatial Atlas: Compute-Grounded Reasoning for Spatial-Aware Research Agent Benchmarks

arXiv:2604.12102v1 Announce Type: new Abstract: We introduce compute-grounded reasoning (CGR), a design paradigm for spatial-aware research agents in which every answerable sub-problem is resolved by

Spatial-Spectral Adaptive Fidelity and Noise Prior Reduction Guided Hyperspectral Image Denoising

arXiv:2604.12600v1 Announce Type: new Abstract: The core challenge of hyperspectral image denoising is striking the right balance between data fidelity and noise prior modeling. Most existing methods

local-aiarxiv-cs-cv

Speaker effects in language comprehension: An integrative model of language and speaker processing

arXiv:2412.07238v3 Announce Type: replace Abstract: The identity of a speaker influences language comprehension through modulating perception and expectation. This review explores speaker effects and

SpecBound: Adaptive Bounded Self-Speculation with Layer-wise Confidence Calibration

arXiv:2604.12247v1 Announce Type: cross Abstract: Speculative decoding has emerged as a promising approach to accelerate autoregressive inference in large language models (LLMs). Self-draft methods, w

Applications

SpecBranch: Speculative Decoding via Hybrid Drafting and Rollback-Aware Branch Parallelism

arXiv:2506.01979v4 Announce Type: replace-cross Abstract: Recently, speculative decoding (SD) has emerged as a promising technique to accelerate LLM inference by employing a small draft model to propo

applicationsarxiv-cs-ai

StableSketcher: Enhancing Diffusion Model for Pixel-based Sketch Generation via Visual Question Answering Feedback

arXiv:2510.20093v2 Announce Type: replace-cross Abstract: Although recent advancements in diffusion models have significantly enriched the quality of generated images, challenges remain in synthesizin

StoryScope: Investigating idiosyncrasies in AI fiction

model-releasesarxiv-cs-cl

arXiv:2604.03136v4 Announce Type: replace Abstract: As AI-generated fiction becomes increasingly prevalent, questions of authorship and originality are becoming central to how written work is evaluate

Stress Detection Using Wearable Physiological and Sociometric Sensors

arXiv:2604.12746v1 Announce Type: new Abstract: Stress remains a significant social problem for individuals in modern societies. This paper presents a machine learning approach for the automatic detec

StructDiff: A Structure-Preserving and Spatially Controllable Diffusion Model for Single-Image Generation

arXiv:2604.12575v1 Announce Type: new Abstract: This paper introduces StructDiff, a generative framework based on a single-scale diffusion model for single-image generation. Single-image generation ai

tutorialsarxiv-cs-cv

Style-Decoupled Adaptive Routing Network for Underwater Image Enhancement

arXiv:2604.12257v1 Announce Type: new Abstract: Underwater Image Enhancement (UIE) is essential for robust visual perception in marine applications. However, existing methods predominantly rely on uni

Subcritical Signal Propagation at Initialization in Normalization-Free Transformers

arXiv:2604.11890v1 Announce Type: new Abstract: We study signal propagation at initialization in transformers through the averaged partial Jacobian norm (APJN), a measure of gradient amplification acr

SubFlow: Sub-mode Conditioned Flow Matching for Diverse One-Step Generation

arXiv:2604.12273v1 Announce Type: cross Abstract: Flow matching has emerged as a powerful generative framework, with recent few-step methods achieving remarkable inference acceleration. However, we id

tutorialsarxiv-cs-cv

Subspace-Guided Feature Reconstruction for Unsupervised Anomaly Localization

arXiv:2309.13904v3 Announce Type: replace Abstract: Unsupervised anomaly localization aims to identify anomalous regions that deviate from normal sample patterns. Most recent methods perform feature m

Applications

Surrogate models for diffusion on graphs via sparse polynomials

arXiv:2502.06595v3 Announce Type: replace-cross Abstract: Diffusion kernels over graphs have been widely utilized as effective tools in various applications due to their ability to accurately model th

applicationsarxiv-cs-lg

Synthetic POMDPs to Challenge Memory-Augmented RL: Memory Demand Structure Modeling

arXiv:2508.04282v3 Announce Type: replace Abstract: Recent benchmarks for memory-augmented reinforcement learning (RL) have introduced partially observable Markov decision process (POMDP) environments

SynthPix: A lightspeed PIV image generator

arXiv:2512.09664v2 Announce Type: replace-cross Abstract: We describe SynthPix, a synthetic image generator for Particle Image Velocimetry (PIV) with a focus on performance and parallelism on accelera

T2I-BiasBench: A Multi-Metric Framework for Auditing Demographic and Cultural Bias in Text-to-Image Models

arXiv:2604.12481v1 Announce Type: new Abstract: Text-to-image (T2I) generative models achieve impressive visual fidelity but inherit and amplify demographic imbalances and cultural biases embedded in

Task Alignment: A simple and effective proxy for model merging in computer vision

arXiv:2604.12935v1 Announce Type: new Abstract: Efficiently merging several models fine-tuned for different tasks, but stemming from the same pretrained base model, is of great practical interest. Des

safetyarxiv-cs-cv

TCL: Enabling Fast and Efficient Cross-Hardware Tensor Program Optimization via Continual Learning

model-releasesarxiv-cs-lg

arXiv:2604.12891v1 Announce Type: new Abstract: Deep learning (DL) compilers rely on cost models and auto-tuning to optimize tensor programs for target hardware. However, existing approaches depend on

Teaching LLMs Human-Like Editing of Inappropriate Argumentation via Reinforcement Learning

arXiv:2604.12770v1 Announce Type: new Abstract: Editing human-written text has become a standard use case of large language models (LLMs), for example, to make one's arguments more appropriate for a d

safetyarxiv-cs-cl

Technical Report -- A Context-Sensitive Multi-Level Similarity Framework for First-Order Logic Arguments: An Axiomatic Study

arXiv:2604.12534v1 Announce Type: new Abstract: Similarity in formal argumentation has recently gained attention due to its significance in problems such as argument aggregation in semantics and enthy

TEMPLATEFUZZ: Fine-Grained Chat Template Fuzzing for Jailbreaking and Red Teaming LLMs

arXiv:2604.12232v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly deployed across diverse domains, yet their vulnerability to jailbreak attacks, where adversarial inputs

TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs

arXiv:2602.18109v2 Announce Type: replace Abstract: Real-time schedulers must reason about tight deadlines under strict compute budgets. We present TempoNet, a reinforcement learning scheduler that pa

local-aiarxiv-cs-lg

Temporal Flattening in LLM-Generated Text: Comparing Human and LLM Writing Trajectories

arXiv:2604.12097v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in daily applications, from content generation to code writing, where each interaction treats the mod

TempR1: Improving Temporal Understanding of MLLMs via Temporal-Aware Multi-Task Reinforcement Learning

arXiv:2512.03963v3 Announce Type: replace Abstract: Enhancing the temporal understanding of Multimodal Large Language Models (MLLMs) is essential for advancing long-form video analysis, enabling tasks

local-aiarxiv-cs-cv

TeRA: Vector-based Random Tensor Network for High-Rank Adaptation of Large Language Models

model-releasesarxiv-cs-lg

arXiv:2509.03234v2 Announce Type: replace Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), have significantly reduced the number of trainable parameters ne

Ternary Logic Encodings of Temporal Behavior Trees with Application to Control Synthesis

arXiv:2604.12092v1 Announce Type: new Abstract: Behavior Trees (BTs) provide designers an intuitive graphical interface to construct long-horizon plans for autonomous systems. To ensure their correctn

safetyarxiv-cs-ro

The A-R Behavioral Space: Execution-Level Profiling of Tool-Using Language Model Agents in Organizational Deployment

arXiv:2604.12116v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as tool-augmented agents capable of executing system-level operations. While existing benchmarks

The Effect of Document Selection on Query-focused Text Analysis

arXiv:2604.12099v1 Announce Type: cross Abstract: Analyses of document collections often require selecting what data to analyze, as not all documents are relevant to a particular research question and

The Enforcement and Feasibility of Hate Speech Moderation on Twitter

arXiv:2604.12289v1 Announce Type: cross Abstract: Online hate speech is associated with substantial social harms, yet it remains unclear how consistently platforms enforce hate speech policies or whet

The Illusion of Fit: Spatially Resolved Assessment of Constitutive Model Validity in Elastography and Physics-Based Inverse Problems

arXiv:2502.07415v2 Announce Type: replace-cross Abstract: Inferring the mechanical properties of soft tissues from measured deformations is a fundamental challenge in elastography. A rarely examined a

local-aiarxiv-cs-lg

The Linear Centroids Hypothesis: How Deep Network Features Represent Data

arXiv:2604.11962v1 Announce Type: new Abstract: Identifying and understanding the features that a deep network (DN) extracts from its inputs to produce its outputs is a focal point of interpretability

The Long-Horizon Task Mirage? Diagnosing Where and Why Agentic Systems Break

arXiv:2604.11978v1 Announce Type: new Abstract: Large language model (LLM) agents perform strongly on short- and mid-horizon tasks, but often break down on long-horizon tasks that require extended, in