Telegram RegisterThe public register of Telegram

Channel

AI & ML Papers

@PaperNexus

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Telegram's recommendations · Cite this entry

33,498subscribers

+17 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 31,623–100,000.

Register entry

Telegram ID-1001695655637
TypeChannel
Username@PaperNexus
DescriptionAdvancing research in Machine Learning – practical insights, tools, and techniques for researchers. Admin: @HusseinSheikho || @Hussein_Sheikho
CreatedBetween 1 December 2021 and 31 March 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live12 August 2026
Measurements held7
Confirmed unchanged2 times, most recently 12 August 2026
On Telegramt.me/PaperNexus

Growth

33,48133,50033,490.57 August 2026 — 33,481 subscribers7 August 2026 — 33,481 subscribers8 August 2026 — 33,488 subscribers9 August 2026 — 33,487 subscribers10 August 2026 — 33,485 subscribers11 August 2026 — 33,500 subscribers12 August 2026 — 33,498 subscribers33,4987 August 202612 August 2026
7 measurements spanning 5 days, net +17. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 33,478–33,503 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
12 Aug 2026, 02:4233,498-2
11 Aug 2026, 00:3633,500+15
10 Aug 2026, 03:5133,485-2
9 Aug 2026, 07:1233,487-1
8 Aug 2026, 04:0833,488+7
7 Aug 2026, 09:4533,481no change
7 Aug 2026, 09:3833,481first reading

Engagement

54 posts held, back to 5 August 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 20 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
1.01%
avg views ÷ 33,498 subscribers
Avg views / post
340
53 posts measured
Reaction rate
0.443%
reactions ÷ views · ER floor
Posts in window
54
of 54 held

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 8 of 53 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 12 August 2026
Posts held54 (5 August 202612 August 2026)
Views total18,017
Reactions total14
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken12 Aug 2026, 21:09 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.

What this channel posts

Photos
7,210
Videos
560
Links
7,910

Lifetime counters from Telegram’s own channel header, read 12 August 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.

Video runtime
0s
Average length
0s

Measured directly from 4 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no mark.

Reaction mix

14 reactions across 8 posts, in 2 distinct kinds. The most used accounts for 78.6% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
1178.6%
👍321.4%

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 8 of the 54 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 14reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 54 most recent posts we hold, published 5 August 2026 to 12 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.

Recent posts

12 Aug 2026, 17:56 UTC125 viewsread 12 August 2026

🔥 Business Arena: Benchmarking LLM Agents in a Realistic Marketplace 💡 The paper introduces Business Arena, a benchmarking environment that evaluates the performance of large language model agents in running a realistic cross border shop. The goal is to assess the ability of these agents to make business decisions and operate a business in a challenging and dynamic market. The environment is grounded in real data fr

12 Aug 2026, 07:56 UTC294 views3 reactionsread 12 August 2026

🔥 N_0-TWAM: Scaling Tactile-Native World-Action Model for Contact-Rich Manipulation 💡 The paper introduces N0-TWAM, a large-scale tactile-native world-action model designed for contact-rich manipulation tasks. The model predicts future vision, contact, and actions using a unified force-based tactile representation and an asymmetric mixture-of-transformers architecture. The researchers pre-trained N0-TWAM on a large

3

12 Aug 2026, 07:56 UTC216 views1 reactionsread 12 August 2026

🔥 h2oGPT: Democratizing Large Language Models 💡 The paper introduces h2oGPT, a suite of open-source code repositories for creating and using large language models based on generative pretrained transformers. The goal is to provide a truly open-source alternative to closed-source large language models. The authors argue that current large language models pose significant risks such as biased or private text and unaut

1

11 Aug 2026, 21:56 UTC296 views2 reactionsread 12 August 2026

🔥 Ouroboros: A Self-Developing Frontier Coding Agent with Reviewed Core Evolution 💡 The paper presents Ouroboros, a self-developing coding agent that improves its tools, prompts, context assembly, and core implementation through reviewed commits that become the runtime for later work. The core evolution proceeds in two modes: recursive free evolution, where improvement is itself a task, and experience-driven core ev

2

11 Aug 2026, 21:55 UTC216 viewsread 12 August 2026

🔥 BDH-CQ: In-Context Learning with Recurrent Latent Reasoning 💡 The paper introduces BDH-CQ, a reasoning model that combines in-context learning with recurrent latent reasoning. The model is designed to solve complex problems through iterative computation in a high-dimensional latent space, without explicitly stating its intermediate reasoning steps. The key innovation is the use of recurrent memory that is updated

11 Aug 2026, 21:15 UTC167 viewsread 12 August 2026
Forwarded from @Udemy26Photo

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Showing the 12 most recent of 54 posts we hold for @PaperNexus. 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.

Forward network

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Mentions

Names

Channels on the register whose handles appear in this channel's posts.

A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.

Appears in Telegram’s recommendations for other channels

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.

Great Learning Academy
@GreatLearningAcademy · 157,583
Telegram ranks this channel #28 of 83 here — alongside 82 others — read 12 August 2026
Linkedin Learning
@linkedin_learning · 218,111
Telegram ranks this channel #48 of 65 here — alongside 64 others — read 11 August 2026

This channel appears in 2 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.

Cite this entry

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 12 August 2026 — this entry's latest reading, not the date you are reading this.

“AI & ML Papers” (@PaperNexus), 33,498 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/PaperNexus.

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.