DailyPapers ✓

@HuggingPapers · Anywhere · joined 31 Mar 2025

Tweeting interesting papers submitted at https://t.co/rXX8x0HzXV. Submit your own at https://t.co/QhbJKXBd4Q, and link models/datasets/demos to it!

21 443Followers

4Following

7 234Posts total

7.7KViews on collected posts

Neueste Beiträge

DailyPapers ✓@HuggingPapers22 Sep

RoboDawn: Transferring the Intelligence of VLMs to Robotic Control Tsinghua and Tencent Hunyuan show a frozen VLM can drive robots through simple move, rotate and gripper commands, reaching 73.6% one-shot success on RoboTwin 2.0 C2R. https://t.co/3qNPoxKTHv

543 views · 2 likes · 0 reposts · 2 replies Open on X →

DailyPapers ✓@HuggingPapers22 Sep

@HuggingPapers Its 8 blocks with top-2 per token, so the sparse bit didnt go anywhere 31 views · 1 likes · 0 reposts · 1 replies Open on X →

DailyPapers ✓@HuggingPapers22 Sep

@HuggingPapers 游戏评测常被压成一个分，或长 rollout 噪声很大。GameHorizon 把短中长指令拆开，用约 5000 小时、21 款 AAA 对齐，再看离线题能不能扛住逐步在线玩。这需要认真学习一下，同时还需要有模型来验证效果。 15 views · 0 likes · 0 reposts · 0 replies Open on X →

DailyPapers ✓@HuggingPapers22 Sep

Paper: https://t.co/lf9WtBoDow Project: https://t.co/m3qcZ7qJzW Code: https://t.co/1KVeJqX72R Dataset and benchmark coming soon on Hugging Face. 398 views · 2 likes · 1 reposts · 0 replies Open on X →

DailyPapers ✓@HuggingPapers22 Sep

Tencent ARC Lab just released GameHorizon Suite A unified data and evaluation suite measuring AAA gameplay capabilities across multiple temporal horizons for VLMs, UMMs, GUI, coding, and game agents. https://t.co/tlQ1t7DQqh

1.4K views · 23 likes · 6 reposts · 2 replies Open on X →

DailyPapers ✓@HuggingPapers22 Sep

Google's RRSI makes self-improving agents that transfer, not overfit RRSI regularizes the harness evolution - annealing edit budgets, leakage critics, noise floors, and pruning - so gains on the evolve set transfer to unseen benchmarks, with up to 14.1 points on the split and ht

1.1K views · 5 likes · 0 reposts · 2 replies Open on X →

DailyPapers ✓@HuggingPapers22 Sep

Alibaba releases OmniVChat A new task for native audio-visual dialogue, where omni models take audio+video directly and reply with text. Comes with a synthesizing engine, a 2,800-dialogue benchmark, and an RL reward design. https://t.co/3gLAT6pTZ4

1.2K views · 3 likes · 0 reposts · 1 replies Open on X →

DailyPapers ✓@HuggingPapers21 Sep

Paper: https://t.co/rTxrZqhaNe Code: https://t.co/gSQMJTLeHz Deployed in AMap, serving hundreds of millions of users with a 60ms latency budget and a 2.4% relative UVCTR gain in online A/B tests. 520 views · 3 likes · 1 reposts · 0 replies Open on X →

DailyPapers ✓@HuggingPapers21 Sep

Alibaba's AMap introduces IntBMoE A block-conditioned Mixture-of-Experts that decouples participation, execution, and materialization: every token gets full expert-pool participation while only a few composable blocks are computed and stored. https://t.co/Qt67KqKsM3

2.4K views · 46 likes · 5 reposts · 6 replies Open on X →

Im Vergleich zu Konten gleicher Größe

9 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 10K–100K Follower. liegt bei Reichweite und Interaktion unter vergleichbaren Konten.

Medianaufrufe543dieses Konto995Median für 10K–100K

Reichweite, %2.53%dieses Konto3.92%Median für 10K–100K

Interaktion, %0.75%dieses Konto1.54%Median für 10K–100K

Kennzahl	Dieses Konto	Median für 10K–100K	Verhältnis
Medianaufrufe pro Beitrag	543	995	0.55×
Reichweite (Aufrufe ÷ Follower)	2.53%	3.92%	0.65×
Interaktionsrate	0.75%	1.54%	0.49×

Weitere Konten dieser Größe → Mit einem anderen Konto vergleichen → Wie diese Vergleichswerte entstehen →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

2.4K21 Sep

520

1.2K22 Sep

1.1K

1.4K

398

543

Last 9 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

2.39%21 Sep

0.77%

0.43%22 Sep

0.71%

2.15%

0.75%

0.00%

6.45%

0.74%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.54%.

What the audience does

Likes54.1%85 in total

Reposts8.3%13 in total

Replies8.9%14 in total

Quotes1.9%3 in total

Bookmarks26.8%42 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Neueste Beiträge

Im Vergleich zu Konten gleicher Größe

Growth & engagement

Views per post

Engagement rate per post

What the audience does

Ähnliche Konten