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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

Latest posts

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 →
@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 →
@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 →
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 →
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 →
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 →
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 →
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 →
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 →

Against accounts of the same size

9 posts from the last 90 days, next to the 10K–100K follower range. below its peers on both reach and engagement.

Median views543this account995median for 10K–100K
Reach, %2.53%this account3.92%median for 10K–100K
Engagement, %0.75%this account1.54%median for 10K–100K
MetricThis accountMedian for 10K–100KRatio
Median views per post5439950.55×
Reach (views ÷ followers)2.53%3.92%0.65×
Engagement rate0.75%1.54%0.49×

Others in this range →   Compare with another account →   How these benchmarks are built →

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
15
31
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.

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