Channel
@data_science_research_papers
On this record: Growth · Engagement · Reactions · Posts · Telegram's recommendations · Cite this entry
3,088subscribers
+26 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001839029413 |
|---|---|
| Type | Channel |
| Username | @data_science_research_papers |
| Created | Between 1 October 2022 and 30 September 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 16 August 2026 |
| Measurements held | 6 |
| Confirmed unchanged | 1 time, most recently 16 August 2026 |
| On Telegram | t.me/data_science_research_papers |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 16 Aug 2026, 21:38 | 3,088 | +12 |
| 13 Aug 2026, 06:34 | 3,076 | +5 |
| 10 Aug 2026, 12:52 | 3,071 | +8 |
| 7 Aug 2026, 21:30 | 3,063 | +1 |
| 7 Aug 2026, 20:02 | 3,062 | no change |
| 7 Aug 2026, 19:29 | 3,062 | first reading |
20 posts held, back to 30 June 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 6 of 10 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 6 August 2026 |
|---|---|
| Posts held | 20 (30 June 2026 – 6 August 2026) |
| Views total | 2,628 |
| Reactions total | 7 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Aug 2026, 20:02 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
19 reactions across 12 posts, in 4 distinct kinds. The most used accounts for 63.2% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 12 | 63.2% | |
| 👍 | 3 | 15.8% | |
| 🔥 | 3 | 15.8% | |
| 👏 | 1 | 5.26% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 12 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 19reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 30 June 2026 to 6 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
EnterpriseClawBench: Benchmarking Agents from Real Workplace Sessions 📅 Publication Date: Jun 22, 2026 📑 Paper: https://arxiv.org/pdf/2606.23654.pdf 🔗 Code: https://github.com/huggingface 📝 Description: EnterpriseClawBench presents a benchmark for enterprise agents based on real-world sessions with 852 reproducible tasks, emphasizing comprehensive evaluation metrics beyond single performance scores.
❤1
OpenRath: Session-Centered Runtime State for Agent Systems 📅 Publication Date: Jun 17, 2026 📑 Paper: https://arxiv.org/pdf/2606.19409.pdf 🔗 Code: https://github.com/huggingface 📝 Description: OpenRath introduces a PyTorch-like programming model for multi-agent systems using Session as a central runtime abstraction that enables explicit fork, merge, and replay operations while recording comprehensive execution s…
MemSlides: A Hierarchical Memory Driven Agent Framework for Personalized Slide Generation with Multi-turn Local Revision 📅 Publication Date: Jun 15, 2026 📑 Paper: https://arxiv.org/pdf/2606.17162.pdf 🔗 Code: https://github.com/huggingface 📝 Description: MemSlides presents a hierarchical memory framework for personalized presentation agents that separates long-term user profiles, working memory for session const…
❤1
Formalizing Latent Thoughts: Four Axioms of Thought Representation in LLMs 📅 Publication Date: May 7, 2026 📑 Paper: https://arxiv.org/pdf/2606.27378.pdf 🔗 Code: N/A 📝 Description: An axiomatic evaluation framework reveals systematic failures in latent thought representations of LLMs across multiple reasoning tasks, demonstrating that current representations fail to satisfy fundamental functional axioms consisten…
❤2
EnterpriseClawBench: Benchmarking Agents from Real Workplace Sessions 📅 Publication Date: Jun 22, 2026 📑 Paper: https://arxiv.org/pdf/2606.23654.pdf 🔗 Code: https://github.com/huggingface 📝 Description: EnterpriseClawBench presents a benchmark for enterprise agents based on real-world sessions with 852 reproducible tasks, emphasizing comprehensive evaluation metrics beyond single performance scores.
Heterogeneous Scientific Foundation Model Collaboration 📅 Publication Date: Apr 30, 2026 📑 Paper: https://arxiv.org/pdf/2604.27351.pdf 🔗 Code: https://github.com/Violet24K/Eywa 📝 Description: Eywa is a heterogeneous agentic framework that extends language-centric systems to scientific foundation models by integrating domain-specific models with language-based reasoning interfaces for improved performance across …
🔥1
OpenSearch-VL: An Open Recipe for Frontier Multimodal Search Agents 📅 Publication Date: May 6, 2026 📑 Paper: https://arxiv.org/pdf/2605.05185.pdf 🔗 Code: https://github.com/shawn0728/OpenSearch-VL 📝 Description: OpenSearch-VL presents an open-source framework for training advanced multimodal search agents using reinforcement learning, featuring specialized data curation, diverse tool environments, and a novel tr…
WorldOlympiad: Can Your World Model Survive a Triathlon? 📅 Publication Date: Jun 9, 2026 📑 Paper: https://arxiv.org/pdf/2606.11129 💻 Project Page: https://alibaba-damo-academy.github.io/WorldOlympiad/ 📝 Description: The paper introduces WorldOlympiad, a comprehensive benchmark for evaluating video-based world models. The problem with current generative models is that they often focus on visual quality, but lack …
❤1
Qwen-AgentWorld: Language World Models for General Agents 📅 Publication Date: Jun 23, 2026 📑 Paper: https://arxiv.org/pdf/2606.24597.pdf 🔗 Code: https://github.com/huggingface 📝 Description: Language-based world models enable agentic environment simulation across multiple domains and enhance general agent performance through scalable simulation and improved downstream task performance.
HERMES++: Toward a Unified Driving World Model for 3D Scene Understanding and Generation 📅 Publication Date: Apr 30, 2026 📑 Paper: https://arxiv.org/pdf/2604.28196.pdf 🔗 Code: https://github.com/H-EmbodVis/HERMESV2 📝 Description: HERMES++ combines 3D scene understanding and future geometry prediction through BEV representation, LLM-enhanced queries, temporal linking, and joint geometric optimization for autonom…
👍1
ABot-Earth 0.5: Generative 3D Earth Model 📅 Publication Date: Jun 8, 2026 📑 Paper: https://arxiv.org/pdf/2606.09967 💻 Project Page: https://abot-earth.amap.com/ 📝 Description: The paper presents ABot-Earth 0.5, a generative framework that creates realistic 3D environments from satellite imagery. The problem addressed is the need for large-scale 3D reconstruction, which is currently expensive and technically chal…
❤2👏1
OpenSeeker-v2: Pushing the Limits of Search Agents with Informative and High-Difficulty Trajectories 📅 Publication Date: May 5, 2026 📑 Paper: https://arxiv.org/pdf/2605.04036.pdf 🔗 Code: https://github.com/PolarSeeker/OpenSeeker 📝 Description: A simple supervised fine-tuning approach achieves state-of-the-art performance in deep search capabilities using minimal data, outperforming complex industrial pipelines a…
Showing the 12 most recent of 20 posts we hold for @data_science_research_papers. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 1 seed channel's Telegram-generated recommendation list in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 16 August 2026 — this entry's latest reading, not the date you are reading this.
“Data science research papers” (@data_science_research_papers), 3,088 subscribers as measured 16 August 2026. Telegram Register, tgregister.com/channel/data_science_research_papers.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.