Search: “models”

ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport

arXiv:2602.07235v2 Announce Type: replace-cross Abstract: Watermarking is an important tool for promoting the responsible use of large language models (LLMs). Existing watermarks insert a signal into

researcharxiv-cs-ai

Are Targeted Data Poisoning Attacks as Effective as We Think?

DGX agent

arXiv:2509.06896v2 Announce Type: replace Abstract: Targeted data poisoning attacks manipulate model predictions on specific test samples by injecting malicious data into training. Yet existing evalua

researcharxiv-cs-lg

25 May 2026

As X, Do Y: How Persona and Task Combine in Instruction-Tuned LLMs

arXiv:2605.23147v1 Announce Type: cross Abstract: Role prompts of the form As X, do Y admit a clean linear decomposition at one specific site in the residual stream: the prompt-to-answer transition --

b9311

local-aillama-cpp-releases

b9311 is a release build version of llama.cpp, an open-source C/C++ framework for large language model inference. The project enables LLM inference in C/C++ , offering optimized performance across var

BOHM: Zero-Cost Hierarchical Attribution for Compound AI Systems

arXiv:2605.22866v1 Announce Type: new Abstract: Compound AI systems route tasks through hierarchies of specialised components. Attribution is dominated by Shapley-based methods (SHAP), which decompose

Canadians be like come to our Toronto tech week party

Toronto Tech Week is an annual event in Toronto, Canada that brings together technology professionals, entrepreneurs, and innovators for networking, learning, and celebration of the local tech ecosyst

model-releasescohere--x

CHASD: Language Increment-Calibrated Contrastive Decoding against Hallucination in LVLMs

arXiv:2605.23344v1 Announce Type: cross Abstract: Large Vision-Language Models have shown strong multimodal reasoning capabilities, yet they remain susceptible to object hallucinations when language p

local-aiarxiv-cs-ai

CHRONOS: Temporally-Aware Multi-Agent Coordination for Evolving Data Marketplaces

arXiv:2605.23887v1 Announce Type: cross Abstract: Temporal knowledge-graph data marketplaces face three coupled failures in static designs: stale hybrid index shortcuts reduce recall as edges evolve,

Composing People Together: Iterative Pose-Image Generation for Multi-Person Interaction Scenes

arXiv:2605.23178v1 Announce Type: new Abstract: Despite recent progress, text-to-image models still struggle to generate semantically diverse and compositionally accurate multi-person interaction scen

safetyarxiv-cs-cv

Computable Fairness: Boltzmann-Softmax Control for AI Resource Allocation

arXiv:2605.22827v1 Announce Type: cross Abstract: In large-scale AI systems, allocating scarce resources such as GPU compute time and bandwidth among multiple agents is a critical challenge. Conventio

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization

arXiv:2605.23391v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) for coupled multiphysics systems suffer systematic accuracy degradation as inter-equation coupling strengthens.

Deja Vu in Plots: Leveraging Cross-Session Evidence with Retrieval-Augmented LLMs for Live Streaming Risk Assessment

arXiv:2601.16027v2 Announce Type: replace Abstract: The rise of live streaming has transformed online interaction, enabling massive real-time engagement but also exposing platforms to complex risks su

local-aiarxiv-cs-ai

Design and Report Benchmarks for Knowledge Work

arXiv:2605.23262v1 Announce Type: new Abstract: The development of LLM agents has led to a growing body of work on knowledge-work AI, including coding, research, and healthcare. However, current knowl

Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation

arXiv:2605.22950v1 Announce Type: cross Abstract: Score matching is an alternative to maximum likelihood estimation when the normalizing constant is unknown or too costly to evaluate. However, vanilla

Efficient Gradient Estimation for Parameterized Quantum Systems with Lie Algebraic Symmetries

arXiv:2404.05108v3 Announce Type: replace-cross Abstract: Gradient estimation is a central challenge in training parameterized quantum circuits (PQCs) for hybrid quantum-classical optimization and lea

Energy per Successful Goal: Goal-Level Energy Accounting for Agentic AI Systems

arXiv:2605.22883v1 Announce Type: new Abstract: Current AI energy benchmarks measure consumption at the granularity of a single model invocation or training run. For classical single-turn workloads th

safetyarxiv-cs-ai

Entropy Equivalence Testing

arXiv:2605.23225v1 Announce Type: cross Abstract: We introduce the problem of entropy equivalence testing for probability distributions, a relaxation of the well-studied closeness testing problem, whe

Evaluating Memory Structure in LLM Agents

model-releasesarxiv-cs-cl

arXiv:2602.11243v2 Announce Type: replace-cross Abstract: Modern LLM-based agents and chat assistants rely on long-term memory frameworks to store reusable knowledge, recall user preferences, and augm

Exploitation of KnowledgeDeliver via ViewState Deserialization Vulnerability

model-releasesgoogle-cloud-ai

Written by: Takahiro Sugiyama, Peter Revelant, Mathew Potaczek Introduction In late 2025, Mandiant responded to a security incident involving a compromised web server running KnowledgeDeliver. Knowled

Fast-dDrive: Efficient Block-Diffusion VLM for Autonomous Driving

arXiv:2605.23163v1 Announce Type: new Abstract: End-to-end autonomous driving via Vision-Language-Action (VLA) models demands a precarious balance between high-fidelity trajectory planning and efficie

safetyarxiv-cs-cl

FastKernels: Benchmarking GPU Kernel Generation in Production

arXiv:2605.23215v1 Announce Type: cross Abstract: LLM-based agents for GPU kernel generation are advancing rapidly, yet their progress is fundamentally constrained by the benchmarks they optimize agai

FuRA: Full-Rank Parameter-Efficient Fine-Tuning with Spectral Preconditioning

arXiv:2605.22869v1 Announce Type: new Abstract: Both full fine-tuning (Full FT) and parameter-efficient fine-tuning methods such as LoRA introduce weight updates without accounting for the spectral st

GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs

arXiv:2605.23078v1 Announce Type: cross Abstract: Mixture-of-Experts Large Language Models (MoE-LLMs) achieve strong performance but incur substantial memory overhead due to massive expert parameters.

researcharxiv-cs-cl

Applications

GeoMAE: Masking Representation Learning for Spatio-Temporal Graph Forecasting with Missing Values

arXiv:2508.14083v3 Announce Type: replace-cross Abstract: The ubiquity of missing data in urban intelligence systems, attributable to adverse environmental conditions and equipment failures, poses a s

applicationsarxiv-cs-ai

Graph Alignment Topology as an Inductive Bias for Grounding Detection

arXiv:2605.22963v1 Announce Type: cross Abstract: Large Language Models (LLMs) are optimized to produce distributionally plausible continuations rather than to explicitly verify whether generated prop

safetyarxiv-cs-ai

. @GrooveJonesXR needed to deliver the impossible: giant NFL-licensed Crocs parachuting into Dick's Sporting Goods parking lots; hyper-reali…

. @GrooveJonesXR needed to deliver the impossible: giant NFL-licensed Crocs parachuting into Dick's Sporting Goods parking lots; hyper-realistic, multi-location, vertical 9:16 on a holiday deadline. A

model-releasescomfyui--x

HawkesLLM: Semantic Uncertainty Propagation in Agentic Text Simulation

arXiv:2605.23043v1 Announce Type: new Abstract: Agentic text-simulation systems write in sequence, with each item becoming possible context for later steps. That makes uncertainty path-dependent: an e

local-aiarxiv-cs-cl

Hermes Agent now can orchestrate the @OpenHandsDev agents with a new optional skill! `hermes update` then do `hermes skills install official…

model-releasesnous-research--x

Hermes Agent now can orchestrate the @OpenHandsDev agents with a new optional skill! `hermes update` then do `hermes skills install official/autonomous-ai-agents/openhands` Reminder: You can already d

HTMuon: Improving Muon via Heavy-Tailed Spectral Correction

arXiv:2603.10067v2 Announce Type: replace-cross Abstract: Muon has recently shown promising results in LLM training. In this work, we study how to further improve Muon. We argue that Muon's orthogonal

✅Implicit caching is now live on Qwen3.7-Max — kicks in automatically, no setup needed. ⚡️Faster + cheaper out of the box. Need higher, more…

✅Implicit caching is now live on Qwen3.7-Max — kicks in automatically, no setup needed. ⚡️Faster + cheaper out of the box. Need higher, more deterministic hit rates? Try explicit caching instead. 🙌 🔗B

model-releasesqwen--x

In his ~43,000-word encyclical, the Pope urged governments to slow down AI development and decried 'new forms of slavery' in AI and tech supply chains (Joshua McElwee/Reuters)

Joshua McElwee / Reuters: In his ~43,000-word encyclical, the Pope urged governments to slow down AI development and decried “new forms of slavery” in AI and tech supply chains — Pope Leo urged govern

model-releasestechmeme

Inductive Deductive Synthesis: Enabling AI to Generate Formally Verified Systems

arXiv:2605.23109v1 Announce Type: new Abstract: AI agents increasingly excel at generating, testing, and refining code. However, they fall short on tasks requiring formal guarantees of full coverage t

Instrumentation for Imitation Learning: Enhancing Training Datasets for Clothes Hanger Insertion

arXiv:2605.23847v1 Announce Type: new Abstract: Large behaviour models have transformed the field of robotic manipulation, but prohibitive data requirements have thus far prevented a revolution simila

safetyarxiv-cs-ro

IntentScore: Intent-Conditioned Action Evaluation for Computer-Use Agents

arXiv:2604.05157v2 Announce Type: replace Abstract: Computer-Use Agents (CUAs) leverage large language models to execute GUI operations on desktop environments, yet they generate actions without evalu

safetyarxiv-cs-ai

Applications

Is a Document Educational or Just Wikipedia-Style? -- Pitfalls of Classifier-Based Quality Filtering

arXiv:2605.23721v1 Announce Type: new Abstract: Classifier-based Quality Filtering has recently emerged as a fundamental technique in constructing pre-training corpora. The ability to deploy a single

applicationsarxiv-cs-cl

Just found that if you scroll down in the Claude Code app on iPhone… Clawd starts jumping and walking around looking for apps to build

model-releasesboris-cherny--x

The Claude Code app for iPhone features an interactive Easter egg where scrolling down triggers an animated character named 'Clawd' that jumps and walks around the screen, appearing to search for apps

Learning Safely Without Knowing the World:COMPASS-Hedge

arXiv:2603.22348v3 Announce Type: replace Abstract: Online learning algorithms often face a fundamental trilemma: balancing regret guarantees between adversarial and stochastic settings and providing

MAS-Orchestra: Understanding and Improving Multi-Agent Reasoning Through Holistic Orchestration and Controlled Benchmarks

arXiv:2601.14652v5 Announce Type: replace Abstract: While multi-agent systems (MAS) promise elevated intelligence through coordination of agents, current approaches to automatic MAS design under-deliv

Metadata Predictability Is Not Evidence Dependence: An Intervention-Based Audit for Weak-Label Benchmarks

model-releasesarxiv-cs-cl

arXiv:2605.23701v1 Announce Type: new Abstract: We study a protocol-level test for weak-label benchmarks: whether benchmark outputs change when the provided evidence is intervened on. Metadata-only sh

Moonwalk: Inverse-Forward Differentiation

arXiv:2402.14212v4 Announce Type: replace-cross Abstract: Backpropagation's main limitation is its need to store intermediate activations (residuals) during the forward pass, which restricts the depth

Non-normal spectral signatures of instability in neural network training dynamics

arXiv:2605.23476v1 Announce Type: new Abstract: Training instabilities in deep networks - loss spikes, oscillatory convergence, and gradient pathologies - are empirically prevalent but lack a rigorous

One Policy, Infinite NPCs: Persona-Traceable Shared RL Policies for Scalable Game Agents

arXiv:2605.23652v1 Announce Type: new Abstract: On a 300-persona life-simulation benchmark, pcsp achieves compositional zero-shot persona identification up to 17x above chance, Spearman rho approx 0.7

OnePred: Next-Query Prediction via Recursive Intent Memory in Multi-Turn Conversations

arXiv:2605.23668v1 Announce Type: cross Abstract: Although large language model (LLM) conversational systems process millions of multi-turn dialogues daily, they remain fundamentally reactive: they re

researcharxiv-cs-ai

Online Partitioned Local Depth for semi-supervised applications

arXiv:2512.15436v2 Announce Type: replace-cross Abstract: We introduce an extension of the partitioned local depth (PaLD) algorithm that is adapted to online applications such as semi-supervised predi

Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems

arXiv:2605.23297v1 Announce Type: new Abstract: AI-enabled services deployed in critical digital infrastructure are subject to governance obligations spanning transparency, accountability, fairness, a

Optimization of randomized neural networks for transfer operator approximation

arXiv:2605.23689v1 Announce Type: new Abstract: RaNNDy is a randomized neural network architecture for the data-driven approximation of transfer operators associated with complex dynamical systems. Th

Order-Optimal Sequential 1-Bit Mean Estimation in General Tail Regimes

arXiv:2604.07796v2 Announce Type: replace-cross Abstract: In this paper, we study the problem of mean estimation under 1-bit communication constraints. We propose a novel adaptive mean estimator based

PathCal: State-Aware Reflection-Marker Calibration for Efficient Reasoning

arXiv:2605.23074v1 Announce Type: new Abstract: The emergence of Large Reasoning Language Models (LRMs) has paved the way for tackling complex reasoning tasks through test-time scaling by generating l

researcharxiv-cs-ai