All entries

Took some inspiration from @vboykis and converted my first ever talk into a blog post. I talk about the role of agentic search in context en…

Took some inspiration from @vboykis and converted my first ever talk into a blog post. I talk about the role of agentic search in context engineering. Together we build an intuition on the strengths a

model-releasesswyx--x

Model Releases

Tool Forge: A Validation-Carrying Toolchain for Governed Agentic Execution

DGX agent

arXiv:2605.28000v1 Announce Type: cross Abstract: Large language model agents are increasingly expected to perform operational work: calling APIs, manipulating files, assembling workflows, and acting

model-releasesarxiv-cs-ai

28 May 2026

Toward Robust Semi-supervised Regression via Dual-stream Knowledge Distillation

arXiv:2508.14082v3 Announce Type: replace Abstract: Semi-supervised regression (SSR), which aims to predict continuous scores for samples while reducing the reliance on large-scale labeled data, has r

safetyarxiv-cs-lg

Toward Semantic-Agnostic and Shape-Aware Vision-Language Segmentation Models

arXiv:2605.28348v1 Announce Type: new Abstract: Vision-language segmentation models have recently achieved strong performance by leveraging high-level semantic object categories expressed in natural l

applicationsarxiv-cs-cv

Towards automated data analysis: A guided framework for LLM-based risk estimation

arXiv:2603.04631v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly integrated into critical decision-making pipelines, a trend that raises the demand for robust and auto

Towards Faithful Agentic XAI: A Verification Method and an Open-World Benchmark for Better Model Faithfulness

arXiv:2605.27879v1 Announce Type: new Abstract: Explainable AI (XAI) helps users interpret model behavior and identify potential faults. Agentic XAI systems use Large Language Models (LLMs) to make ex

Towards Reliable Multilingual LLMs-as-a-Judge: An Empirical Study

arXiv:2605.28710v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for the automatic evaluation of generated text, yet most prior work focuses on English. Despite the

researcharxiv-cs-ai

Towards Unified Vision-Language Models with Incomplete Multi-Modal Inputs

arXiv:2605.27894v1 Announce Type: new Abstract: Video-Language Models (VLMs) have demonstrated impressive multi-modal reasoning capabilities across diverse computer vision applications. However, these

safetyarxiv-cs-cv

TRACER: Turn-level Regret Matching with Inner Reinforcement Credit for Cooperative Multi-LLM Reasoning

arXiv:2605.28699v1 Announce Type: new Abstract: Large language models increasingly rely on either reinforcement learning or multi-agent prompting to improve reasoning, yet these two paradigms remain d

TRACES: Proactive Safety Auditing for Multi-Turn LLM Agents via Trajectory-State Modeling

arXiv:2605.27690v1 Announce Type: new Abstract: LLM agents increasingly operate through multi-turn tool use and environment interaction, where safety risks often emerge from intermediate steps long be

safetyarxiv-cs-cl

Training Azerbaijani language models on Amazon SageMaker AI

Azercell Telecom LLC, Azerbaijan's leading telecommunications provider, wanted to build an Azerbaijani large language model (LLM) on Amazon SageMaker AI for telecom use cases and a customer-facing cha

applicationsaws-ml-blog

Training Stratigraphy: Persistent Behavioral Artifacts in Large Language Models Observed Through Longitudinal AI-Human Interaction

arXiv:2605.28102v1 Announce Type: new Abstract: Large language models trained with Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI exhibit persistent behavioral patterns that s

Transfer learning RGB models to hyperspectral images with trainable tensor decompositions

arXiv:2605.28331v1 Announce Type: new Abstract: Transfer learning makes it possible to use large vision networks on a variety of domains, by specializing their models' general filters to new tasks. Ho

researcharxiv-cs-cv

Transferable Graph Condensation from the Causal Perspective

arXiv:2601.21309v4 Announce Type: replace Abstract: The increasing scale of graph datasets has significantly improved the performance of graph representation learning methods, but it has also introduc

Transferable Reinforcement Learning via Probabilistic Latent Embeddings and Dynamic Policy Adaptation for Sim-to-Real Deployment

arXiv:2605.27659v1 Announce Type: cross Abstract: Due to limited resources and public safety concerns, deep reinforcement learning (RL) agents for many cyber-physical systems (e.g., autonomous vehicle

Tutorials

Transformers Provably Learn to Internalize Chain-of-Thought

arXiv:2605.28600v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting substantially improves the sample efficiency of transformers, reducing the complexity of tasks like parity learning fro

tutorialsarxiv-cs-lg

Tree of Thoughts as a Classical Heuristic Search Problem: Formal Foundations and Design Patterns

arXiv:2605.28566v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, yet their standard generation process -- auto-regressive token predict

researcharxiv-cs-ai

Triangular-Reference Schrodinger Bridges for Time Series Generation

arXiv:2605.27478v1 Announce Type: cross Abstract: We introduce Triangular-Reference Schrodinger Bridges for Time Series (TR-SBTS), a conservative extension of the SBTS framework in which the Brownian

Trinity: Unifying Class-Agnostic Terrain and Semantic Segmentation for Unstructured Outdoor Environments by Leveraging Synthetic Data

arXiv:2605.27644v1 Announce Type: cross Abstract: Terrain understanding is fundamental for mobile robots operating in unstructured outdoor environments. Existing vision-based traversability estimation

Industry

Triomics, which is building an AI-powered platform to help oncologists automate data-heavy tasks, raised a 22M Series B, following a 15M Series A in 2024 (Marina Temkin/TechCrunch)

Marina Temkin / TechCrunch: Triomics, which is building an AI-powered platform to help oncologists automate data-heavy tasks, raised a 22M Series B, following a 15M Series A in 2024 — Triomics, a star

industrytechmeme

Trump loses more control over AI regulation as Illinois passes landmark law

Illinois' House of Representatives passed SB 315, a landmark bill requiring frontier AI companies like OpenAI and Anthropic to create, publish and annually update plans addressing severe or catastroph

safetyars-technica

Trust Me, I'm an Expert: Decoding and Steering Authority Bias in Large Language Models

arXiv:2601.13433v3 Announce Type: replace Abstract: Prior research demonstrates that performance of language models on reasoning tasks can be influenced by suggestions, hints and endorsements. However

safetyarxiv-cs-cl

Local Ai

Trust Region Continual Learning as an Implicit Meta-Learner

arXiv:2602.02417v2 Announce Type: replace Abstract: Continual learning aims to acquire tasks sequentially without catastrophic forgetting, yet standard strategies face a core tradeoff: regularization-

local-aiarxiv-cs-lg

Local Ai

Turning people into DnD characters for AI comic

local-air-stablediffusion

This project uses Stable Diffusion AI to generate D&D character artwork from uploaded character sheets , streamlining the character creation process for tabletop gamers. The AI-generated avatars make

Turning Video Models into Generalist Robot Policies

arXiv:2605.27817v1 Announce Type: cross Abstract: Video generative models have emerged as a promising robotics backbone, capable of generating videos that depict the completion of complex tasks across

turns out i wasn’t wrong:

Gary Marcus reflects on a past prediction or stance that he held, asserting its correctness in retrospect, likely addressing criticisms or skepticism he previously faced regarding AI, cognitive scienc

safetygary-marcus--x

Agents

two weeks ago i didn’t know what event sourcing was and now i see it everywhere

Event sourcing is an architectural pattern that stores application state changes as a sequence of immutable events rather than just persisting the current state, enabling a complete audit trail and ab

agentsyohei-nakajima--x

Industry

UK researchers win access to Google's Willow quantum chip, which it says completes a calculation in five minutes that takes supercomputers 10 septillion years (Chris Vallance/BBC)

Chris Vallance / BBC: UK researchers win access to Google's Willow quantum chip, which it says completes a calculation in five minutes that takes supercomputers 10 septillion years — Scientists from K

industrytechmeme

Agents

Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Study

arXiv:2506.08311v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) agents leverage Large Language Models (LLMs) to autonomously diagnose and fix software bugs through reasoning,

agentsarxiv-cs-ai

Understanding Generalization and Forgetting in In-Context Continual Learning

model-releasesarxiv-cs-lg

arXiv:2605.28705v1 Announce Type: new Abstract: In-context learning (ICL) derives its power from enabling Large Language Models to adapt to new tasks via prompt-based reasoning alone, entirely bypassi

Hardware

Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation

arXiv:2605.27582v1 Announce Type: cross Abstract: Embodied navigation requires an agent to map language and visual observations to a stream of spatial actions that drive a real robot through environme

hardwarearxiv-cs-cv

Unification and Optimization of Robust Supervised Learning

arXiv:2605.28165v1 Announce Type: new Abstract: The literature has proposed various robust alternatives to empirical risk minimisation to address failure modes such as distribution shift, label noise

Unified Multi-Domain Graph Pre-training for Homogeneous and Heterogeneous Graphs via Domain-Specific Expert Encoding

arXiv:2602.13075v2 Announce Type: replace Abstract: Graph pre-training has achieved remarkable success in recent years, delivering transferable representations for downstream adaptation. However, most

applicationsarxiv-cs-lg

Unified Synthesis of Compositional Speech and Sound from Free-Form Text Prompts

arXiv:2605.28063v1 Announce Type: cross Abstract: Audio generation has made significant progress, yet synthesizing unified audio where speech and sounds are naturally composited remains a challenge. C

Unifying Low Dimensional Spectra in Deep Learning

arXiv:2404.06106v3 Announce Type: replace Abstract: Low dimensional structures appear ubiquitously in the eigenspectra of deep learning matrices in classification networks trained in the overparameter

UniMaia: Steering Chess Policies with Language for Human-like Play

arXiv:2605.27767v1 Announce Type: cross Abstract: Recent advances in large language models have enabled natural language to serve as a flexible interface for controlling complex systems, but often at

UNIQUE: Universal Top-k Sparse Attention for Training-free Inference and Sparsity-aware Training

arXiv:2605.27740v1 Announce Type: new Abstract: Long-context inference in large language models (LLMs) is bottlenecked by the linear growth of the self-attention key-value (KV) cache. Top-k sparse att

researcharxiv-cs-cl

Industry

Unity Catalog and the next era of Apache Iceberg™

Unity Catalog, Databricks' open governance solution, integrates with Apache Iceberg to provide unified metadata management, access controls, and data lineage across data lakes and warehouses. This int

industrydatabricks

Universal Time Series Generation with Neural Controlled Differential Equations

arXiv:2605.28507v1 Announce Type: new Abstract: Recent work on the sequence universality of State Space Models (SSMs) has introduced efficient, maximally expressive continuous-time approaches for time

Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models

arXiv:2605.27376v1 Announce Type: cross Abstract: While prompt-based text-to-speech (TTS) models enable natural language-driven speaking style control, they often provide limited fine-grained control

Unsupervised Identification and Removal of Spurious Correlations During Fine-Tuning

arXiv:2605.27676v1 Announce Type: cross Abstract: Fine-tuning a pretrained language model on a curated dataset can produce spurious correlations between the fine-tuning task and unintended latent fact

safetyarxiv-cs-lg

US healthcare still stupidly expensive, with pathetic outcomes, study finds

Although the United States spends more on health care than any other country, it consistently underperforms on key metrics. A Commonwealth Fund study evaluating the U.S. health system relative to 19 o

applicationsars-technica

Agents

UserHarness: Harnessing User Minds for Stronger Agent Theory-of-Mind

arXiv:2605.27721v1 Announce Type: cross Abstract: Understanding what a user believes and intends is central to building effective agent assistants. This ability is often evaluated through Theory-of-Mi

agentsarxiv-cs-ai

Using Zero-Shot LLM-Generated Survey Data for Geographically Explicit Population Synthesis

arXiv:2605.27401v1 Announce Type: cross Abstract: There is a growing interest in utilizing synthetic populations for a diverse range of applications. At the same time, we are witnessing a tremendous g

Hardware

Utilidata raises $40M more to optimize data center power use

Utilidata Inc. today disclosed that it has raised 40 million in funding from Renown Capital Partners and Keyframe Capital. The cash infusion comes as an extension to a 60.3 million Series C round the

hardwaresiliconangle

Utility-Aware Multimodal Contrastive Learning for Product Image Generation

arXiv:2605.28733v1 Announce Type: new Abstract: Product images strongly influence consumer decision-making in online marketplaces. Empowered by multimodal contrastive learning, generative AI can outpu

Local Ai

v0.30.0-rc29

Ollama v0.30.0-rc29 is a pre-release version that changes the architecture to directly support llama.cpp instead of building on top of GGML, and adds compatibility with the GGUF file format. MLX is us

local-aiollama-releases

Variance-Adaptive Optimal Algorithm for Reinforcement Learning with Multinomial Logit Function Approximation

arXiv:2605.28364v1 Announce Type: cross Abstract: Reinforcement learning with multinomial logistic (MNL) function approximation has become an important framework due to its flexibility and broad appli