rjakob

It’s less about proof and more about demonstrating a new capability that TSLMs enable. To be fair, the paper did test standard LLMs, which consistently underperformed. @iLoveOncall, can you point to examples where out of the box models achieved good results on multiple time-series? Also, what kind of time-series data did you analyze with Claude 3.5? What exactly did you predict, and how did you assess reasoning capabilities?

Thanks for the note. Ironically, the post is about models built to understand time.

Foundation models excel at text, images, audio, and video, but lack temporal reasoning capabilities over time-series data streams that run the real world: vitals, prices, telemetry, grid loads, clickstreams, machine logs, business processes.

Time Series Language Models (TSLMs) are open foundation models, supporting time‑series as a native modality, next to text, letting users ask questions, get explanations, and recommendations, all in natural language.

The OpenTSLM White Paper released today demonstrates state-of-the-art temporal reasoning performance. Unlike prior approaches, the cross-attention architecture scales to long time-series remaining viable at scale.

The results:

- Sleep staging: 4.4× accuracy with a model 200× smaller (~880× efficiency)

- Activity recognition: ~6× accuracy with 200× smaller (~1,000× efficiency)

- ECG interpretation: ~2× accuracy with 200× smaller (~400× efficiency)

— first model to process 12-lead ECG signals and text simultaneously with chain-of-thought reasoning validated by cardiologists.

For the first time, foundation models can handle multiple time-series streams of varying lengths concurrently, integrate them with textual context, and produce interpretable explanations (verified by domain experts, clinicians).

This work is the result of a growing collaboration between researchers from Stanford, ETH Zurich, UIUC, University of St. Gallen, University of Washington, Google, and Amazon.

It points to the next foundation model frontier: temporal intelligence that unlocks proactive healthcare, adaptive robotics, resilient infrastructure, and new forms of human-AI collaboration.

OpenTSLM:https://www.opentslm.com/ Stanford Repo: https://github.com/StanfordBDHG/OpenTSLM

If you know the trick to getting reviewed in a day, do tell. Asking for an entire field.

inspired by friends at Browser-Use

whenever you would like to have comprehensive feedback on your manuscript (more likely during pre-submission or after publishing a preprint).

noted.

We also provide feedback on rigor across 7 different categories: https://github.com/robertjakob/rigorous/tree/main/Agent1_Pee...

System prompts / review criteria cannot be "leaked" because they are open-source (full transparency). Focusing heavily on monetization at this stage seems shortsighted...this tool is a small (but longterm important) step of a larger plan.

As mentioned above, there is an open-source version for those who want full control. The free cloud version is mainly for convenience and faster iteration. We don’t store manuscript files longer than necessary to generate feedback (https://www.rigorous.company/privacy), and we have no intention of using manuscripts for anything beyond testing the AI reviewer.

Then let's try to be the least biased and fully transparent (which should also help with bias)

Here is a description of how it works: https://github.com/robertjakob/rigorous/tree/main/Agent1_Pee...

Best feedback so far!

You're right: In the current version each "agent" essentially loads the paper, applies a specialized prompt, and calls the OpenAI API. The specialization lies in how each prompt targets a specific dimension of peer review (e.g., methodological soundness, novelty, citation quality). While it’s not specialization via architecture yet (i.e., different models), it’s prompt-driven specialization, essentially simulating a review committee, where each member is focused on a distinct concern. We’re currently using a long-context, cost-efficient model (GPT-4.1-nano style) for these specialized agents to keep it viable for now. Think of it as an army of reviewers flagging areas for potential improvement.

To synthesize and refine feedback, we also run Quality Control agents (acting like an associate editor), which reviews all prior outputs from the individual agents to reduce redundancy and surface the most constructive insights (and filter out less relevant feedback).

On your point about nitpicking: we’ve tested the system on several well-regarded, peer-reviewed papers. While the output is generally reasonable, there are occasional instances where feedback is misaligned. We're convinced we can almost fully reduce such noise in future iterations.

On the code side: 100% agree. This is very much an MVP focused on testing potential value to researchers, and the repeated agent classes were helpful for fast iteration. However, your suggestion of switching to template-based prompt loading and dynamic agent registration is great and would improve maintainability and scalability. We'll 100% consider it in the next version.

The _determine_research_type method is indeed a stub. Good catch. Also, lol @ the JS comment hashes, touché.

If you're open to contributing or reviewing, we’d love to collaborate!

We are already looking into that: https://github.com/robertjakob/rigorous/tree/main/Agent2_Out...

Would be great to see contributions from the community!

...or run it themselves. The code is open source: https://github.com/robertjakob/rigorous

Note: The current version uses the OpenAI API, but it should be adaptable to run on local models instead.

Cool! We'll get back asap.

We'd be happy to hear what kind of feedback you find useful, what is useless, and what you would want in an ideal review report. (https://docs.google.com/forms/d/1EhQvw-HdGRqfL01jZaayoaiTWLS)

Wouldn't that just require a robust, predefined ruleset we could all agree on? Let's make the dream come true!

We honestly didn’t think much about the term “AI peer reviewer” and didn’t mean to imply it’s equivalent to human peer review. We’ll stick to using “AI reviewer” going forward.

I wish my own manuscripts would be that important...

Regarding security concerns: there is an open-source version for those who want full control. The free cloud version is mainly for convenience and faster iteration. We don’t store manuscript files longer than necessary to generate feedback (https://www.rigorous.company/privacy), and we have no intention of using manuscripts for anything beyond testing the AI reviewer.

Thanks for the thoughtful feedback. That’s very helpful.

We didn’t think too deeply about the term “AI peer reviewer” and didn’t mean to imply it’s equivalent to human peer review. Based on your comments, we’ll stick to using “AI reviewer” going forward.

Regarding security: there is an open-source version for those who want full control. The free cloud version is mainly for convenience and faster iteration. We don’t store manuscript files longer than necessary to generate feedback (https://www.rigorous.company/privacy), and we have no intention of using manuscripts for anything beyond testing the AI reviewer.

On novelty. totally agree it’s a core part of good peer review. The current version actually includes agents evaluating originality, contribution, impact, and significance. It’s still v1 of course but we want to improve it. We'd actually love for critical thinkers like you to help shape it. If you're open to testing it with a preprint and sharing your thoughts on the feedback, that would be extremely valuable to us.

Thanks again for engaging, we really appreciate it.

Fair. Though in this case, it was obvious even without a detector.

Thanks for the heads-up. We'll raise the file size limit shortly.

Based on my experience, many reviewers are already using AI extensively. I recently ran reviewer feedback from a top CS conference through an AI detector, and two out of three responses were clearly flagged as AI-generated.

In my view, the peer-review process is flawed. Reviewers have little incentive to engage meaningfully. There’s no financial compensation, and often no way to even get credit for it. It would be cool to have something like a Google Scholar page for reviewers to showcase their contributions and signal expertise.

NOTE: We've received a bunch of submissions from you all (which is awesome — thank you!).

We're working through them and will send out reports asap!

Since we're currently covering the model costs for you, we'd appreciate any feedback via this short form in return: https://docs.google.com/forms/d/1EhQvw-HdGRqfL01jZaayoaiTWLS...

Thanks again for testing!

Once you receive the report, we'd really appreciate your feedback on what we can improve via https://docs.google.com/forms/d/1EhQvw-HdGRqfL01jZaayoaiTWLS...

Haha fair point, domain name was a 5-second, “what’s available for $6” kind of decision. Definitely not trying to go full corporate just yet

Good point. Current focus is on improving AI feedback quality, not business model. But we’ll definitely consider local model support for privacy-conscious users. Thanks for the input!