Channel
@RIMLLab
On this record: Growth · Engagement · Posts · Citations · Cite this entry
3,141subscribers
+10 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
| Telegram ID | -1001623987582 |
|---|---|
| Type | Channel |
| Username | @RIMLLab |
| Created | Between 1 December 2021 and 31 March 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 13 August 2026 |
| Measurements held | 4 |
| Confirmed unchanged | 1 time, most recently 13 August 2026 |
| On Telegram | t.me/RIMLLab |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 13 Aug 2026, 11:26 | 3,141 | +8 |
| 10 Aug 2026, 07:40 | 3,133 | +4 |
| 7 Aug 2026, 02:07 | 3,129 | -2 |
| 6 Aug 2026, 06:32 | 3,131 | first reading |
20 posts held, back to 25 November 2025 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 3 pagesof 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.
| Window | Rolling 30 days · latest post in window 26 July 2026 |
|---|---|
| Posts held | 20 (25 November 2025 – 26 July 2026) |
| Views total | 10,410 |
| Reactions total | — |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 7 Aug 2026, 23:55 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.
📢 Research Collaboration in Quantitative Finance at RIML We are seeking motivated students interested in quantitative finance, stochastic modeling, machine learning, and portfolio optimization. Selected researchers will work under the supervision of Dr. Rohban and collaborate with international professors and researchers affiliated with the University of Manchester, the Alan Turing Institute, Virginia Tech, and the …
آزمایشگاه RIML تحت نظارت دکتر رهبان در حال راهاندازی یک ژورنالکلاب پیرامون یادگیری تقویتی چندعاملی (Multi-Agent RL) بر پایهی کتاب Albrecht با چشمانداز حرکت به سمت کار پژوهشی جدی در این حوزه است. در صورت علاقهمندی به این مسیر، خواهشمندست این فرم را پر کنید. *: جلسههای ژورنالکلاب از این هفته آغاز میشود. در صورتی که پرسش یا ابهامی در این زمینه دارید با شناسهی زیر در تلگرام ارتباط بگیرید: @Moein_Salimi
🔐 LLM Faithfulness Journal Club ✅ This Week's Presentation: 🔹 Title: Verbosity Tradeoffs and the Impact of Scale on the Faithfulness of LLM Self-Explanations 🔸 Presenter: [Farzan Rahmani](https://www.linkedin.com/in/farzan-rahmani-51128b201) 🌀 Abstract: When large language models explain their decisions, their explanations may sound convincing—but do they faithfully reflect the model's true reasoning? This paper …
🤖 RL Journal Club ✅ This Week's Presentation: 🔹 Title: Is Reinforcement Learning Really Harder Than Bandits? 🔸 Presenter: Arshia Gharooni 🌀 Abstract: Episodic reinforcement learning, despite having longer planning horizons, presents little additional sample complexity difficulty compared to contextual bandits, with the proposed Monotonic Value Propagation (MVP) algorithm achieving near-optimal regret bounds. The M…
🤖 RL Journal Club ✅ This Week's Presentation: 🔹 Title: Test Time Exploration to Achieve Generalization in Zero-Shot RL 🔸 Presenter: Alireza Farajtabrizi 🌀 Abstract: This paper studies zero-shot generalization in reinforcement learning, where an agent is trained on a set of tasks but must perform well on unseen test environments. The authors argue that standard reward-maximizing RL agents can overfit to training …
🚀 Open Research Position: Visual Reasoning in Large Vision-Language Models (LVLMs) We are looking for motivated students to join our research on visual reasoning in Large Vision-Language Models (LVLMs) at RIML Lab. 🔍 Project Description Large Vision-Language Models have achieved remarkable performance across a wide range of multimodal tasks. However, their ability to perform complex visual reasoning remains an ope…
🔐 LLM Faithfulness Journal Club ✅ This Week's Presentation: 🔹 Title: Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety 🔸 Presenter: [Farzan Rahmani](https://www.linkedin.com/in/farzan-rahmani-51128b201) 🌀 Abstract: AI systems that “think” in human language offer a unique opportunity for AI safety: we can monitor their chains of thought (CoT) for the intent to misbehave. Like all other k…
🔐 ML Security Journal Club ✅ This Week's Presentation: 🔹 Title: A Machine Unlearning Approach to Safety Alignment 🔸 Presenter: Arian Komaei 🌀 Abstract: The paper identifies a fundamental limitation in current vision language model (VLM) alignment called the "safety mirage." Traditional supervised safety fine-tuning often reinforces superficial textual patterns rather than deep harm mitigation, leaving models vul…
🔘 Open Research Positions: Shortcut Learning and Spurious Correlation We are looking for motivated students to join our research projects. 🔍 Project Description Shortcut learning occurs when models rely on spurious or overly simple patterns instead of learning the intended underlying task. Our research aims to understand why and how shortcuts emerge, and how they can be identified, analyzed, and mitigated. We offer…
دانشجویانی که علاقه مند به دستیاری آموزشی درس یادگیری تقویتی دکتر رهبان در نیم سال جاری هستند لطفا فرم زیر را پر کنند. https://docs.google.com/forms/d/e/1FAIpQLSe2rPGlxxTQDzDnt6PB_hRAtBa64_OLmWLE_dUnPqQlAfpUqQ/viewform?usp=dialog
We invite interested collaborators to join an ongoing research project that aims to redefine attention mechanisms in large language models, drawing inspiration from the concept of consciousness. The goal of this work is to produce results suitable for submission to NeurIPS 2026. This research is conducted under the supervision of Dr. Rohban and is led by his PhD student, Hamidreza Akbari. Motivated undergraduate stud…
دانشجویانی که علاقهمند به دستیاری آموزشی درس سیستم ۲ (دکتر رهبان، دکتر سلیمانی و آقای سمیعی) هستند، خواهشمند است فرم زیر را تکمیل نمایند: تکمیل فرم
Showing the 12 most recent of 20 posts we hold for @RIMLLab. 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.
Citation-graph rank — 362,074 of 1,176,251entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
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.
Named by 2 registered channels — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
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.
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 13 August 2026 — this entry's latest reading, not the date you are reading this.
“RIML Lab” (@RIMLLab), 3,141 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/RIMLLab.
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.