56 Billion Parameters Shocking Open Source! Meituan LongCat Leads the Way: A New Ceiling in the Field of Mathematical Proof - AIBase
<a href="https://news.google.com/rss/articles/CBMiSkFVX3lxTFBBS0hvZzFNNXlGSVF4NjhtTG9Ya19lVmVUNVozaEpoYjdRSVprd1RTYXZOQ2FFV25HYndtd3pYM0NfMGVLSEY4aG9B?oc=5" target="_blank">56 Billion Parameters Shocking Open Source! Meituan LongCat Leads the Way: A New Ceiling in the Field of Mathematical Proof</a> <font color="#6f6f6f">AIBase</font>
Could not retrieve the full article text.
Read on Google News - Meituan AI →Google News - Meituan AI
https://news.google.com/rss/articles/CBMiSkFVX3lxTFBBS0hvZzFNNXlGSVF4NjhtTG9Ya19lVmVUNVozaEpoYjdRSVprd1RTYXZOQ2FFV25HYndtd3pYM0NfMGVLSEY4aG9B?oc=5Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
open sourcebillion
My most common research advice: do quick sanity checks
Written quickly as part of the Inkhaven Residency . At a high level, research feedback I give to more junior research collaborators often can fall into one of three categories: Doing quick sanity checks Saying precisely what you want to say Asking why one more time In each case, I think the advice can be taken to an extreme I no longer endorse. Accordingly, I’ve tried to spell out the degree to which you should implement the advice, as well as what “taking it too far” might look like. This piece covers doing quick sanity checks, which is the most common advice I give to junior researchers. I’ll cover the other two pieces of advice in a subsequent piece. Doing quick sanity checks Research is hard (almost by definition) and people are often wrong. Every researcher has wasted countless hours
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Releases

HippoMM: Hippocampal-inspired Multimodal Memory for Long Audiovisual Event Understanding
arXiv:2504.10739v2 Announce Type: replace-cross Abstract: Comprehending extended audiovisual experiences remains challenging for computational systems, particularly temporal integration and cross-modal associations fundamental to human episodic memory. We introduce HippoMM, a computational cognitive architecture that maps hippocampal mechanisms to solve these challenges. Rather than relying on scaling or architectural sophistication, HippoMM implements three integrated components: (i) Episodic Segmentation detects audiovisual input changes to split videos into discrete episodes, mirroring dentate gyrus pattern separation; (ii) Memory Consolidation compresses episodes into summaries with key features preserved, analogous to hippocampal memory formation; and (iii) Hierarchical Memory Retriev

ToolMisuseBench: An Offline Deterministic Benchmark for Tool Misuse and Recovery in Agentic Systems
arXiv:2604.01508v1 Announce Type: new Abstract: Tool using agents often fail for operational reasons even when language understanding is strong. Common causes include invalid arguments, interface drift, weak recovery, and inefficient retry behavior. We introduce ToolMisuseBench, an offline deterministic benchmark for evaluating tool misuse and recovery under explicit step, call, and retry budgets. The benchmark covers CRUD, retrieval, file, and scheduling environments with replayable fault injection. It reports success, invalid call behavior, policy violations, recovery quality, and budgeted efficiency. We release a public dataset with 6800 tasks and a reproducible evaluation pipeline. Baseline results show fault specific recovery gains for schema aware methods, while overall success remai

GAP-URGENet: A Generative-Predictive Fusion Framework for Universal Speech Enhancement
arXiv:2604.01832v1 Announce Type: new Abstract: We introduce GAP-URGENet, a generative-predictive fusion framework developed for Track 1 of the ICASSP 2026 URGENT Challenge. The system integrates a generative branch, which performs full-stack speech restoration in a self-supervised representation domain and reconstructs the waveform via a neural vocoder, along with a predictive branch that performs spectrogram-domain enhancement, providing complementary cues. Outputs from both branches are fused by a post-processing module, which also performs bandwidth extension to generate the enhanced waveform at 48 kHz, later downsampled to the original sampling rate. This generative-predictive fusion improves robustness and perceptual quality, achieving top performance in the blind-test phase and rank

MOVis: A Visual Analytics Tool for Surfacing Missed Patches Across Software Variants
arXiv:2604.01494v1 Announce Type: new Abstract: Clone-and-own development produces families of related software variants that evolve independently. As variants diverge, important fixes applied in one repository are often missing in others. PaReco has shown that thousands of such missed opportunity (MO) patches exist across real ecosystems, yet its textual output provides limited support for understanding where and how these fixes should be propagated. We present MOVis, a lightweight, interactive desktop tool that visualizes MO patches between a source and target variant. MOVis loads PaReco's MO classifications and presents patched and buggy hunks side-by-side, highlighting corresponding regions and exposing structural differences that hinder reuse. This design enables developers to quickly




Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!