Search: “models”

RubricMiddleware helps your agent verify task completion with a grader subagent This is similar to /goal in claude code or codex, but for de…

model-releasesharrison-chase--x

RubricMiddleware is a LangChain feature that enables agents to verify task completion by delegating grading to a specialized subagent using predefined rubrics. This approach parallels the goal-verific

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Safety

Safety Alignment of LMs via Non-cooperative Games

DGX agent

arXiv:2512.20806v3 Announce Type: replace Abstract: Ensuring the safety of language models (LMs) while maintaining their usefulness remains a critical challenge in AI alignment. Current approaches rel

safetyarxiv-cs-ai

2 Jun 2026

SALSA: Speech Aware LLM Adaptation via Learned Steering Activation Vectors

arXiv:2606.00460v1 Announce Type: new Abstract: Speech-aware large language models often generalize poorly to out-of-domain settings. We propose SALSA (Speech-Aware LLM Adaptation via Learned Steering

researcharxiv-cs-cl

SARA: Stress Test Reasoning in Audio Deepfake Detection

arXiv:2601.03615v2 Announce Type: replace Abstract: Audio Language Models (ALMs) offer a promising shift towards explainable audio deepfake detections (ADD), moving beyond extit{black-box} classifiers

researcharxiv-cs-cl

Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach

arXiv:2503.24183v3 Announce Type: replace Abstract: The expansion of ride-sourcing services such as Uber and Lyft has reshaped urban transportation by offering flexible, on-demand mobility via mobile

safetyarxiv-cs-lg

Science Earth: Towards A Planet-Scale Operating System for AI-Native Scientific Discovery

arXiv:2606.01316v1 Announce Type: new Abstract: Scientific discovery demands intelligence, perseverance, and serendipity across vast search spaces. Today, top scientific capabilities remain siloed--on

SeClaw: Spec-Driven Security Task Synthesis for Evaluating Autonomous Agents

arXiv:2606.02302v1 Announce Type: cross Abstract: Autonomous LLM agents increasingly operate in stateful environments where they access tools, files, memory, and external services. While such capabili

Self-Imitated Diffusion Policy for Efficient and Robust Visual Navigation

model-releasesarxiv-cs-ro

arXiv:2601.22965v2 Announce Type: replace Abstract: Diffusion policies (DP) have demonstrated significant potential in visual navigation by capturing diverse multi-modal trajectory distributions. Howe

Sensor Tower: ChatGPT has become the fastest app to hit 1B global MAUs by far; ChatGPT's MAUs are up 62% YoY in Q2 to date, Claude's MAUs are up 640% YoY to 56M (Harshita Mary Varghese/Reuters)

Harshita Mary Varghese / Reuters: Sensor Tower: ChatGPT has become the fastest app to hit 1B global MAUs by far; ChatGPT's MAUs are up 62% YoY in Q2 to date, Claude's MAUs are up 640% YoY to 56M — Ope

model-releasestechmeme

Shape Your Body: Value Gradients for Multi-Embodiment Robot Design

arXiv:2606.00702v1 Announce Type: cross Abstract: We propose to turn generalist multi-embodiment value functions into reusable models for robot design. Instead of running a new reinforcement learning

Simultaneous Multi-objective Alignment Across Verifiable and Non-verifiable Rewards

arXiv:2510.01167v2 Announce Type: replace-cross Abstract: Aligning large language models to human preferences is inherently multidimensional, yet most pipelines collapse heterogeneous signals into a s

SkillHarm: Lifecycle-Aware Skill-Based Attacks via Automated Construction

model-releasesarxiv-cs-cl

arXiv:2606.02540v1 Announce Type: new Abstract: Agent skills occupy a privileged position in the agent workflow, as agents are expected to implicitly follow and execute them, rendering third-party ski

SkillPager: Query-Adaptive Intra-Skill Navigation via Semantic Node Retrieval

arXiv:2606.00822v1 Announce Type: cross Abstract: Skill-based LLM agents increasingly rely on long procedural documents, but full-document prompting wastes tokens and dilutes information critical to e

SkyShield: Occupancy as a Safety Interface for Low-Altitude UAV Autonomy

arXiv:2606.00747v1 Announce Type: cross Abstract: For low-altitude Unmanned Aerial Vehicle (UAV) autonomy, 3D spatial understanding is not merely a perception objective, but the safety interface betwe

Soft-NBCE: Entropy-Weighted Chunk Fusion for Long-Context

arXiv:2606.01101v1 Announce Type: cross Abstract: The quadratic complexity of self-attention remains a bottleneck for Large Language Models (LLMs) processing ultra-long contexts. The Naive Bayes Cogni

researcharxiv-cs-ai

SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering

arXiv:2606.00593v1 Announce Type: cross Abstract: Large language models are increasingly deployed as tool-augmented agents to acquire information beyond parametric knowledge. While recent work has imp

Sparse Autoencoders for Interpretable Emotion Control in Text-to-Speech

arXiv:2606.01479v1 Announce Type: new Abstract: Integrating large language models (LLMs) into text-to-speech (TTS) systems has improved speech expressiveness, yet interpretable emotional control remai

researcharxiv-cs-cl

Sparse FEONet: A Low-Cost, Memory-Efficient Operator Network via Finite-Element Local Sparsity for Parametric PDEs

arXiv:2601.00672v2 Announce Type: replace-cross Abstract: In this paper, we study the finite element operator network (FEONet), an operator-learning method for parametric problems, originally introduc

Spatially Distributed Task-Oriented Compression for Multi-Emitter Localization and Characterization with Spectral Overlap

arXiv:2606.01446v1 Announce Type: cross Abstract: Radio frequency spectrum awareness requires the ability to detect, localize, and characterize emitters in dense and contested wireless environments. I

Spectra-Guided Neural Tucker Factorization

arXiv:2606.00584v1 Announce Type: cross Abstract: This paper proposes Spectra-Guided Neural Tucker Factorization (SG-NTF) for High-Dimensional and Incomplete (HDI) tensor completion. Circumventing dis

SS-ZKR: Spatial-Semantic Zero-Knowledge Routing for Privacy-Preserving Multi-Agent Collaboration

arXiv:2606.00962v1 Announce Type: cross Abstract: Foundational agent interoperability standards, notably the Agent-to-Agent (A2A) protocol and the Model Context Protocol (MCP), have advanced multi-age

Stability Analysis of Sharpness-Aware Minimization

arXiv:2301.06308v2 Announce Type: replace-cross Abstract: Sharpness-aware minimization (SAM) is a training method that seeks to find flat minima in deep learning, resulting in state-of-the-art perform

Step-Level Sparse Autoencoder for Reasoning Process Interpretation

arXiv:2603.03031v2 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved strong complex reasoning capabilities through Chain-of-Thought (CoT) reasoning. However, their reasoning

researcharxiv-cs-lg

Tutorials

Strong Stochastic Flow Maps

arXiv:2606.01086v1 Announce Type: cross Abstract: Flow and diffusion models generate high-quality samples in many modalities; however, many network evaluations are required during inference due to num

tutorialsarxiv-cs-ai

Structured Semantic Information Helps Retrieve Better Examples for In-Context Learning Applied to Few-Shot Relation Extraction

model-releasesarxiv-cs-cl

arXiv:2601.20803v2 Announce Type: replace Abstract: This paper presents several strategies to automatically obtain additional examples for in-context learning, effectively transforming relation extrac

SuperMemory-VQA: An Egocentric Visual Question-Answering Benchmark for Long-Horizon Memory

model-releasesarxiv-cs-cv

arXiv:2606.00825v1 Announce Type: new Abstract: AI glasses present a compelling platform for AI agents to serve as personalized memory assistants. To be genuinely useful, such systems must move beyond

Hardware

SUPREME: A Multi-GPU Framework for Reproducible Image Unlearning Method Evaluation

arXiv:2606.00380v1 Announce Type: cross Abstract: Machine unlearning removes the influence of specific training data from a trained model without retraining it from scratch. Evaluating an unlearning m

hardwarearxiv-cs-ai

Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data

arXiv:2606.00298v1 Announce Type: cross Abstract: Data-driven reduced-order modeling is an essential component in the computer-aided design of control systems. In this work, we present a novel symmetr

researcharxiv-cs-lg

Applications

Symmetries in PAC-Bayesian Learning

arXiv:2510.17303v2 Announce Type: replace Abstract: Symmetries are known to improve the empirical performance of machine learning models, yet theoretical guarantees explaining these gains remain limit

applicationsarxiv-cs-lg

Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems

arXiv:2606.00282v1 Announce Type: cross Abstract: Large-scale recommendation systems operate across diverse domains, yet they face the challenges of data sparsity and noisy implicit feedback. Traditio

researcharxiv-cs-ai

Local Ai

Systematic Evaluation of Time-Frequency Features for Binaural Sound Source Localization

arXiv:2511.13487v3 Announce Type: replace-cross Abstract: This study presents a systematic evaluation of time-frequency feature design for binaural sound source localization (SSL), focusing on how fea

local-aiarxiv-cs-lg

Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults

arXiv:2505.19489v2 Announce Type: replace Abstract: The Linux kernel is a critical system, serving as the foundation for numerous systems. Bugs in the Linux kernel can cause serious consequences, affe

Task diversity produces systematic transfer but inhibits continual reinforcement learning

arXiv:2606.00880v1 Announce Type: cross Abstract: Continual reinforcement learning aims to produce agents that learn not only to improve at their current tasks but also to adapt as task distributions

Task-Induced Representational Invariances Depend on Learning Objective in Deep RL

arXiv:2606.01868v1 Announce Type: new Abstract: Reinforcement Learning (RL) has long served as a model for goal-directed animal behavior in neuroscience. Modern deep RL has shown remarkable success ac

safetyarxiv-cs-lg

TextFake: Benchmarking AI-Generated Image Detection on Text-Rich Images

model-releasesarxiv-cs-cv

arXiv:2606.01050v1 Announce Type: new Abstract: Recent AI-generated image (AIGI) detectors perform well on natural-image benchmarks, but their behavior on text-rich forgeries, such as fabricated scree

Agents

The Deterministic Horizon: When Extended Reasoning Fails and Tool Delegation Becomes Necessary

arXiv:2606.00376v1 Announce Type: new Abstract: Extended chain-of-thought reasoning can degrade performance on deterministic state-tracking tasks, not due to preference biases, but limits rooted in th

agentsarxiv-cs-ai

The role of class encoding in neural collapse

arXiv:2606.00344v1 Announce Type: new Abstract: Neural collapse is a structural property of the last-hidden-layer activations in neural network classification models, when trained beyond a zero classi

safetyarxiv-cs-lg

This is also available on the Claude Blog! https://claude.com/blog/a-harness-for-every-task-dynamic-workflows-in-claude-code

This post discusses dynamic workflows in Claude Code, enabling flexible task automation and execution patterns. The content is available on the official Claude Blog and addresses how Claude can be har

model-releasesthariq--x

this is fine 🐶☕️🔥

model-releasesemad-mostaque--x

This post likely references the popular 'This is Fine' meme featuring a dog in a burning room, often used to comment on problematic situations being accepted or ignored. Without access to the specific

Time-Optimal Collision Avoidance Via a Greedy Polynomial Backward Sweep

model-releasesarxiv-cs-ro

arXiv:2606.01169v1 Announce Type: cross Abstract: Spacecraft collision avoidance for low-thrust satellites often requires determining not only how to maneuver, but also how late a maneuver can begin w

TN-SHAP-G: Graph-Structured Tensor Network Surrogates for Shapley Values and Interactions

arXiv:2606.01540v1 Announce Type: cross Abstract: Shapley values are a widely used tool for attributing importance and interactions among input variables in black-box models, but their computation inv

researcharxiv-cs-ai

ToolFG: Towards Well-Grounded Fine-Grained Image Classification

arXiv:2606.02518v1 Announce Type: new Abstract: Fine-grained image classification (FGIC) has broad applications and has attracted significant research attention. In this paper, we explore a novel para

safetyarxiv-cs-cv

Topological Ignorability for Structural Causal Effects Beyond Means

arXiv:2606.01184v1 Announce Type: cross Abstract: Many interventions alter the structure of an outcome distribution rather than its mean: they can split a population into disconnected regimes, create

Local Ai

Torus Graphs for Large Scale Neural Phase Analysis

arXiv:2606.00496v1 Announce Type: new Abstract: Oscillatory neural signals such as electroencephalography (EEG) and local field potentials (LFPs) show phase relationships that coordinate communication

local-aiarxiv-cs-lg

Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance

arXiv:2304.11127v5 Announce Type: replace-cross Abstract: Recent scientific advances require complex experiment design, necessitating the meticulous tuning of many experiment parameters. Tree-structur

TriAlign: Towards Universal Truth Consistency in Personalized LLM Alignment

arXiv:2606.01755v1 Announce Type: new Abstract: Personalized large language models adapt responses to users' preferences and social attributes, but can introduce substantial universal truth inconsiste

Trust Region On-Policy Distillation

arXiv:2606.01249v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) is a fundamental technique for efficient post-training of large language models (LLMs), with broad applications in agent

safetyarxiv-cs-cl

Turning Back Without Forgetting: Selective Backward Refinement for Parameter-Efficient Continual Learning

arXiv:2606.01379v1 Announce Type: new Abstract: While prompt-based parameter-efficient continual learning mitigates catastrophic forgetting by isolating task-specific prompts, this isolation also limi