model releases

arXiv:2608.23898v1 Announce Type: new Abstract: We introduce BenchBench-Protocol, a benchmark for large language models of 149 protocol-modification tasks recovered from modifications that scientists

arXiv:2608.24314v1 Announce Type: new Abstract: Evaluating conversational voice agents at scale re- quires reliable assessment methods that capture both observ- able interaction quality and the contex

arXiv:2608.23653v1 Announce Type: cross Abstract: AI agents are increasingly used for simulation-driven engineering. Physical system modeling presents different requirements from general-purpose code

arXiv:2603.17170v2 Announce Type: replace-cross Abstract: AI agents increasingly execute users' natural-language (NL) tasks by calling Web services, yet today's Web authorizes these calls through OAut

arXiv:2608.24482v1 Announce Type: cross Abstract: Mechanistic Localization bridges mechanistic interpretability and post-training optimization by isolating critical parameters via interpretative appro

Big news: GLM-5.3-Flash by @Zai_org has landed around #5 in the Code Arena: WebDev (#2 among open models) scoring 1634 (AutoEval). Priced at 0.15/0.5 Mtoken, it reshapes the Pareto Frontier! For compa

Big shoutout to @perplexity_ai for properly benchmarking document understanding in their Portable Computer release 🔥 They took a subset of our ParseBench benchmark (https://www.parsebench.ai/) and mea

BREAKING: GLM-5.3 Flash by @Zai_org debuts at 6th overall on Design Arena with a preliminary Elo of 1343. This places the MIT-licensed, natively multimodal model in the same performance band as GLM-5.

arXiv:2511.00260v2 Announce Type: replace Abstract: Anatomy-aware colonoscopic navigation requires localizing partial endoscopic observations on a stable 3D reference to support coverage assessment, r

arXiv:2608.24794v1 Announce Type: new Abstract: Outcome-supervised search agents learn when and how to retrieve evidence, but terminal rewards neither localize intermediate errors nor redirect an ongo

arXiv:2608.23744v1 Announce Type: new Abstract: Split conformal prediction, not the pruning rule, supplies finite-sample marginal coverage once a pruned model is fixed independently of the conformal c

arXiv:2507.21790v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are becoming widely used to support various workflows across different disciplines, yet their potential in discre

arXiv:2608.24033v1 Announce Type: cross Abstract: Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although time series foundation models support fo

Claude is an incredible coding agent. I use it a lot. (Also Codex and start checking Cursor again). But I’m not convinced the AI-native SDLC is the existing SDLC with a magical Claude wand waved over

*part 2 of an earlier post: previous quant comparison with voxel island creation this time I rented three rtx pro 6000 96gb, on each one I launched a qwen 3.8 27b quant and gave them 4 identical promp

arXiv:2504.18346v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been transformative across many domains. However, hallucination, i.e., confidently outputting incorrect info

arXiv:2608.24070v1 Announce Type: new Abstract: Prohibitive computational and environmental costs impede the scalable deployment of Large Language Models (LLMs). Traditional compression techniques (sp

arXiv:2608.23663v1 Announce Type: cross Abstract: Aligning deployed language models requires knowing when their outputs can be trusted, yet on-device models now ship to hundreds of millions of devices

arXiv:2608.24218v1 Announce Type: new Abstract: Enterprise entity alignment must handle semi-structured records, implicit attributes, and unit or granularity mismatches. Manual matching is still commo

arXiv:2608.23903v1 Announce Type: new Abstract: Visual foundation models are commonly adapted under the assumption that the appearance of incoming data may change while the semantic meaning of the pre

arXiv:2608.23847v1 Announce Type: cross Abstract: This paper proposes the Coronavirus Optimization Algorithm (COA), a SARS-CoV-2-inspired success-history adaptive evolutionary optimizer for box-constr

arXiv:2608.23636v1 Announce Type: new Abstract: Small-object detection and instance segmentation remain challenging in orchard environments because of green-on-green similarity, occlusion, and limited

arXiv:2506.12006v4 Announce Type: replace-cross Abstract: The cross-Modality Domain Adaptation (crossMoDA) challenge series, initiated in 2021 in conjunction with the International Conference on Medic

arXiv:2608.23573v1 Announce Type: new Abstract: A trained transformer's weight magnitudes can be summarized by a two-parameter Weibull distribution whose shape k approx 1.2 is stable across layers and

Artificial intelligence startup Deep Cogito Inc. today announced that it has raised 43 million in funding. TQ Ventures led the Series A round. It was joined by Benchmark, Nexus Venture Partners, Atrei

arXiv:2608.24039v1 Announce Type: cross Abstract: Manufacturing process planning transforms heterogeneous design information into coherent manufacturing decisions. However, existing approaches focus o

arXiv:2608.23809v1 Announce Type: cross Abstract: Multilingual language models can solve the same mathematical problem in different languages, but it remains unclear whether they rely on shared featur

arXiv:2608.24439v1 Announce Type: new Abstract: Visual Language Models (VLMs) excel at describing visible scene content but struggle to reason about dynamic multi-agent interactions, where action sema

arXiv:2608.24814v1 Announce Type: new Abstract: We uncover ELR collapse in language model pretraining: learning rate (LR) and parameter norm govern loss dynamics primarily through their ratio, the eff

arXiv:2607.08960v2 Announce Type: replace-cross Abstract: Warehouse operations are governed by Standard Operating Procedures (SOPs) that encode complex, multi-system decision logic, which must be exec

arXiv:2608.24086v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as code agents for scientific and engineering analysis, but their ability to analyze raw physical-lay

arXiv:2608.23569v1 Announce Type: new Abstract: State-of-the-art Natural Language to SQL (NL2SQL) models report execution accuracy exceeding 89 percent on established benchmarks such as Spider and BIR

arXiv:2603.02041v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are predominantly trained on English-centric data, resulting in uneven performance for smaller languages. We stud

Amazon Bedrock AgentCore Evaluations decouples agent evaluation from the framework you build on. As long as your agent emits OpenTelemetry telemetry, the service can score it, whether you use LangGrap

arXiv:2608.23961v1 Announce Type: cross Abstract: Background: Large Language Models (LLMs) have demonstrated strong performance across a variety of code-understanding tasks, leading many to believe th

arXiv:2608.24228v1 Announce Type: new Abstract: Many LLM applications are most useful when they provide several candidate outputs for comparison, validation, or combination. Predominant evaluation set

arXiv:2608.24223v1 Announce Type: new Abstract: Event-based motion estimation is central to tasks that demand high temporal resolution and robustness to fast motion. Existing methods typically rely on

arXiv:2608.23758v1 Announce Type: cross Abstract: Recent large audio language models (LALMs) have achieved impressive progress in audio understanding. However, existing evaluations remain largely cons

Security operations startup Mate Security Ltd. today launched Gamebooks, a set of structured investigation procedures that govern what its artificial intelligence agents are allowed to do while workin

The Qwen3.8‑Flash‑Next 176 B multimodal MoE model uses a hybrid Gated DeltaNet / Qwen Sparse Attention (QSA) architecture that compresses long‑context history and aggregates tokens into micro‑blocks f

Qwen3.8‑Flash‑Next is a multimodal mixture‑of‑experts model with 125 B parameters that employs Gated DeltaNet (compressing historic context) and Qwen Sparse Attention (exact retrieval over full contex

arXiv:2608.23651v1 Announce Type: cross Abstract: Agent harnesses record a failed tool call and its error message in the transcript and ask the model to continue, on the assumption that the error is c

arXiv:2608.23593v1 Announce Type: cross Abstract: Text-to-image systems use learned aesthetic scorers to filter training data and guide generation, but whether these scores encode demographic attribut

As AI takes on more complex work, business leaders face a new challenge: enabling rapid innovation using agents while protecting their margins and budgets. To get a real return on AI, financial operat

https://x.com/romanchernin/status/2092488160680751437?s=20 - Multimodal (Vision) - 1M Tokens Context Window - DeepSWE ~63% EDIT: THE ORIGINAL POST IS GONE Roman Chernin @romanchernin The mysterious Ox

The Artist: Qwen3.8-27B-UD-Q3K_XL, q8_0 caches, xhigh, temp 1.0, image-min-tokens 1024, froggeric template) I was screwing around with different Qwen3.8-27B quants and thought of this very simplistic