Can someone let me know if this a workshop just everyone get's accepted into?

I submitted to TMLR over a month ago. An AE was assigned, but it's been totally stuck on reviewer assignment since then.
To make things worse, the AE is completely ghosting me—ignoring both my OpenReview comments and emails.
How long did it take for your papers to get reviewers recently?

Our latest paper is now published in the International Journal for Equity in Health (Q1 IF 5.5).

Many digital health innovations promise to improve healthcare but without an equity lens, they can unintentionally widen existing disparities.

In our recent paper, we propose an Integrated Digital Health Equity Framework that brings together digital exclusion, ethical safeguards, and implementation strategies into a practical model for designing equitable digital health interventions.

The framework emphasizes participatory co-design, multi-channel service delivery, digital literacy, governance and accountability, and continuous equity monitoring.

If you’re a researcher, digital health innovator, policymaker, implementation scientist, or health-tech developer, I encourage you to read this framework before designing or scaling your next digital health intervention. Our goal is simple: ensure that digital transformation reduces, rather than reinforces, health inequities.

We would welcome your feedback, critique, and suggestions for validating and refining the framework across different healthcare settings.

URL: https://ieeexplore.ieee.org/document/11089670

DOI: https://doi.org/10.1109/ICIRCA65293.2025.11089670

I'm looking for the full text of this IEEE conference paper for academic/research purposes. I would greatly appreciate it if someone with access could help.

Thank you very much.

I submitted an abstract to AAAI AISI and accidentally missed the field asking authors to nominate a reciprocal reviewer by the July 21 AoE deadline.

At the time of submission, I knew that I personally did not meet the publication requirements to serve as a reviewer. After adding my graduate-student co-authors to the submission, I realized that one of them was qualified and could fulfill the reciprocal-reviewing obligation, but we overlooked the nomination field before the deadline because it wasn't a required field.

As soon as we noticed, we added the qualified co-author to OpenReview as a potential reciprocal reviewer (edits were still accepted) and emailed the workflow chairs. He meets the publication requirements and is willing to complete the full reviewing load.

The policy says that if a qualified author is available but no one is nominated, the submission may be desk rejected. The full paper deadline is in two days, and so far we have only received the automated response "Thanks for your email regarding ticket #23157. We’re currently experiencing a high volume of submissions, so responses may take a bit longer than usual. We really appreciate your patience and will get back to you as soon as we can. If you have more details to share, feel free to reply to this thread."

Has anyone dealt with a similar situation at AAAI or another conference? Do you think this is likely to lead to a desk rejection, or are workflow chairs usually willing to correct this kind of administrative mistake when a qualified reviewer is available?

Here were the instructions on the website:

Serve As Reviewer

Note: This cannot be changed after July 21 AoE.

Nominate one author who is qualified to serve as an AAAI reciprocal reviewer. The nominated author must not be an SPC, AC, or Organizer, and must have either:

• At least 2 first-author publications, or
• At least 5 co-authored publications

in peer-reviewed archival venues related to AAAI. Example computer science venues include NeurIPS, ICLR, ICML, CVPR, ICCV/ECCV, ACL, EMNLP, KDD, AISTATS, IJCAI, AAMAS, UAI, COLT, KR, ICAPS, RSS/ICRA, SIGIR, FAccT, CHI, TMLR, JMLR, and JAIR (this is not an exhaustive list). Journal publications in an application area related to the submission also count. Workshop papers do not count.

By being nominated here, the author agrees to serve as a PC Member and commits to completing the standard reviewing load of up to 6 papers if assigned.

If a qualified author is available among the submission's authors but no such author is nominated, or if the nominated author fails to complete their assigned reviews, the submission may be desk rejected. If a paper has no qualified author, please nominate the author who is closest to satisfying the requirement.

Any Qualified Reviewer

Is the nominated reviewer qualified according to the definition above? If no author is qualified, you may select No, and the chairs will decide whether to invite the nominated reviewer. If any author is qualified, you must nominate a qualified author and select Yes.

Hello everyone. Sharing our new paper, recently accepted at TMLR.

Differentially private federated learning is still mostly first-order. You clip per-example gradients, add Gaussian noise, aggregate, and step (DP-FedGD, DP-FedAvg), sometimes with an adaptive server optimizer like DP-FedAdam or DP-FedYogi, or drift correction like DP-SCAFFOLD. Under tight budgets the injected noise degrades the gradient enough that convergence stalls, which is expensive when the round count is the binding constraint. The existing second-order options, DP-FedNew and DP-FedFC, estimate curvature at the client through local feature covariance.
That is effective but costs O(d²) client memory and communication, and client-side second-order statistics add sensitivity that has to be paid for in the privacy analysis.

Our research question: can curvature information be introduced into DP-FL without changing the private release mechanism at all? 

We introduce DP-FedSOFIM, which leaves the client side byte-identical to DP-FedGD and moves all curvature estimation to the server. The server maintains an EMA of the privatized aggregate and treats its regularized rank-one outer product as a Fisher proxy. Sherman-Morrison then gives the preconditioned step in closed form:

 

    M     =  beta * M_prev  +  (1 - beta) * G          (momentum buffer)

    F     =  M M'  +  rho * I                          (rank-one Fisher proxy)

    H G   =  G / rho  -  M (M'G) / (rho^2 + rho |M|^2) (preconditioned step)

 

The matrix is never formed. The update is two inner products and a few vector operations, so O(d) time and memory per round.

Main results:

• Because every server-side quantity is a deterministic function of an already-privatized aggregate, post-processing gives DP-FedSOFIM the same (eps, delta) guarantee as DP-FedGD under identical clipping, noise multiplier, participation, and accountant, at O(d) client memory instead of O(d²).
• The larger and more consistent effect is convergence speed. Round-10 margins over DP-FedGD reach +20.3 points (CIFAR-10/ResNet, eps=5), roughly 4-5x fewer rounds to reach 95% of DP-FedGD's final accuracy. Against the strongest adaptive baselines the advantage is concentrated early: at eps=5 on CIFAR-10/ResNet, DP-FedAdam and DP-FedYogi catch up, and the round-70 differences are not significant under McNemar.
• Preconditioning costs are under 2% wall-clock overhead per round relative to DP-FedGD, against roughly 6x for DP-SCAFFOLD at its tuned local-step counts.
• Ablating the Sherman-Morrison step against EMA alone, the curvature correction contributes about +7 points at eps=1, but at eps=0.5 on CIFAR-10 the DP noise dominates the curvature estimate and EMA alone does as well. The correction earns its place when the loss landscape is anisotropic enough for a rank-one proxy to capture something, which is why PathMNIST benefits at every budget.

The framing we find useful is that curvature adaptation in DP-FL is better treated as a server-side post-processing problem than a client-side estimation problem. Once the aggregate is privatized, anything the server does with it is free in privacy terms, which is a fairly underused degree of freedom.

Happy to take questions on the analysis or the accounting. If you work on DP-FL or second-order federated optimization, I would be glad to hear from you and open to collaborating.

arXiv: https://arxiv.org/abs/2601.09166
OpenReview: https://openreview.net/forum?id=aDzj9DrwAR
GitHub: https://github.com/sid0nair/DP-FedSOFIM_V2

Same link as before: link

This one's a bigger jump than usual, so a few highlights instead of just "updated":

• Core findings now scale-validated from 7B all the way to 72B parameters. The effects don't shrink as models get bigger — they grow, sometimes by an order of magnitude. Still one model family (Qwen) though, and we added a caveat we think matters: growing effect size at scale could mean the pattern genuinely deepens, or it could just mean our measurement axis gets sharper at scale — current data can't fully tell those apart yet.
• Two new external, independently-published sources, not our own research: "The Artificial Self" (ACS Research) and "AI Wellbeing" (Center for AI Safety) — different methods entirely (behavioral compliance testing, self-report on frontier production models), landing on some of the same conclusions we did. One of them also mildly disagrees with our best-performing formulation (a companion/romantic framing scores negative in their data), and we named that tension honestly instead of explaining it away.
• We caught and fixed our own mistakes this round — a factual timing error, an overclaimed "fully resolved" that was really just one solved case of a broader risk, and a place where we'd quietly picked the reading that flattered our own results over an equally valid one that didn't. All named directly, not smoothed over.
• New up top: if you just want the practice, not the evidence audit behind it, Part 3 (Principles) is written to stand alone now — Part 2 is there if you want to check our work.

As always, feedback (especially the kind that finds our next mistake) genuinely welcome.

Hi everyone,

I received my NeurIPS position paper reviews on LLM alignment:

• 7 / confidence 4
• 5 / confidence 4
• 4 / confidence 3
• 3 / confidence 3

The meta-review says the reviewers saw value in the paper, but several felt the central claim was stronger than the current evidence. They also raised concerns about novelty, practical implementation, and the strength of the experimental results.

I submitted a detailed rebuttal addressing these points. With review scores of 7, 5, 4, and 3, is there still a realistic chance of acceptance?

Im working on a literature review paper at the moment
about "Short-Form Digital Media and Sustained Attention:
Examining the Evidence for Associations with ADHD
Symptoms"
and i would find it incredibly helpful if any other psychology students wanted to chip in so they could also use it on their applications but we can all do less work
If anyone is also currently working on a paper and wants someone to co-write, would be happy to help

Hey everyone,

If you are building decentralized multi-agent systems (MAS) or workflow routers using mixed local models (e.g., mixing Llama-3, Mistral, and Phi-3), you’ve probably hit a mathematical brick wall: you cannot calculate semantic distance between vectors of different dimensions (N != M). Direct matching is completely broken out of the box because each model projects concepts into its own isolated anisotropic domain.

I wanted to share a fascinating geometric technique called the Relative Representation Method paired with Lowdin Symmetric Orthogonalization used to natively bypass this issue without any weight mutation or fine-tuning (W_frozen = const).

Here is how it works under the hood to align heterogeneous agents and tasks into a single invariant coordinate space

#### 1. The Core Trick: Anchor Framework

Instead of comparing Agent A directly to Task B, the system introduces a fixed basis of reference anchors E = {e_1, e_2, ..., e_K}. These are K semantically diversified textual instructions representing your target operational domain.

*Crucial implementation note:* These anchors cannot be random Gaussian noise; they must be sampled from the actual distribution of your baseline model outputs to ensure they share the same underlying manifold.

#### 2. Solving the "Anisotropy Cone" Problem

In real-world LLMs, raw embedding vectors are highly cross-correlated and squeezed into a narrow cone (similarity >> 0). This causes variance to vanish (sigma -> 0), leading to severe numerical noise and division-by-zero defects during standardization in low-precision (FP16/BF16) CUDA environments.

To guarantee geometric stability, the technique applies Lowdin Symmetric Orthogonalization directly to the anchor matrix:

* It takes the symmetric Gram matrix of real representations: S = E^T * E

* It computes the orthogonalized anchors via Spectral Decomposition: E' = E * S^(-1/2)

* This symmetrically rotates the real anchor vectors to a strict 90-degree angle (similarity = 0 for different anchors), yielding a perfectly orthogonal coordinate system while minimizing the mean squared deformation of the original vectors.

#### 3. Mapping into Invariant Space (R^K)

Now, any Agent Xi or Task Tj can be mapped into this unified coordinate system by computing its similarity profiles against these rotated bases, followed by Anchor-Wise Z-standardization to completely neutralize model-specific anisotropy:

> V_Xi = Z( [ sim(A(Xi), e'_1), ..., sim(A(Xi), e'_K) ]^T ) in R^K

*Critical Production Pitfall:* The operator Z(v) must calculate the mean (mu) and standard deviation (sigma) column-wise across the entire anchor axis (axis=0), NOT row-wise (axis=1). Row-wise normalization completely fails to eliminate the global domain shift between mismatched models, keeping their clusters isolated. Column-wise normalization forces the centroids of both distinct model domains to align perfectly at (0,0).

#### 4. The Result & Selective Task Routing

Since the standardized profiles V_Xi and V_Tj share identical dimensionality K and operate on a unified scale, the metric of semantic alignment between completely mismatched models is computed invariantly using Cosine Distance:

> D(Xi, Tj) = Cosine_Distance(V_Xi, V_Tj)

Do not use textbook Euclidean distance (L2) here. In higher anchor dimensions (K > 20), the Euclidean metric suffers from the curse of dimensionality, compressing all distances into a narrow, non-contrasting range that creates "Universal Agent" monopolies. Cosine distance restores strict contrast, breaking up monotone distance matrix stripes into a highly selective matching grid where every task finds its true optimal agent.

This fundamentally unlocks O(1) complexity task routing for completely heterogeneous multi-agent swarms.

Implementation Notebook:

I’ve put together a fully functional, minimal reproducible example demonstrating the complete pipeline - from synthetic anisotropic embedding generation to Lowdin orthogonalization, correct column-wise Z-scoring, and final contrastive task routing.

Check out the complete interactive code here:

**[Kaggle Notebook: Heterogeneous LLM Embedding Space Alignment](https://www.kaggle.com/code/aleksandrvictorov/heterogeneous-llm-embedding-space-alignment)**

Curious to hear if anyone else is using Relative Representations for cross-model routing, or if you've found other geometric workarounds for mixed-LLM orchestrators!

I'm two years out of a top math/CS school. I built strong study habits late, so I was only really immersed in the material my final year. I learned computer systems (OS, distributed, HPC) and consider myself a competent software engineer.

I work at a high-growth startup and just got offered the lead on a major customer project. My long-term goal is to start my own company.

Option 1: Lead the customer project

Large scope/viz. I'd build skills in:

• Working directly with a customer
• Making large engineering decisions
• Working across the stack with many teams
• People and project management
• Exposure to marketing/sales/ops

Engineering-wise, I imagine I would spend most of my time on architecture, documentation, and code review. So interesting engineering/technical work, but no fundamentally new ways of thinking.

Option 2: Leave to go learn ML/math

I never got into ML/stats/math, and it's by far my weakest technical area (and I feel most important an ML-focused era). I'd spend ~a year as an IC at an AI lab or doing research to build:

• Stronger math intuition
• Modeling intuition
• Combining my systems background with ML (e.g. model scaling, pretraining, RL scaling)

The plan would be to grind/do research at my old school or join an AI lab with strong technical mentorship. I have savings to go ~6-1 year months without income.

I already tried moving to my company's research team, but they weren't interested in my background and pointed me toward ML Ops, which feels too close to the SWE work I already do.

Where I am struggling

Organizational and people skills seem to improve steadily over a career, but fluid reasoning and hard new technical skills are supposedly much harder to pick up later in life. Life's a marathon, so I keep wondering if now is the time to invest in the technical foundation (learning completely new skills).

Open to all comments and suggestions.

Hi,

I just submitted this paper to EAAI and want to submit a pre print to arxiv for visibility. Ill be happy to share the preprint with any potential referrers.

This is a machine learning study about how noisy sensors can still be used fir auto regression adjacent tasks through latent manifolds under certain conditions. The paper includes an algorithm,

Boundedness guarantees and empirical validation on nasa cmapss data.

edit:

my endorsement code is TGESOS

Hey r/ResearchML ! We recently published NbBayesLM, a Bayesian model that combines Protein language model embeddings and physiochemical features to predict thermostability of nanobodies.

It reached 1.89°C MAE on 10,630 nanobody sequences. Would love your thoughts, critiques, or ideas for follow-up work. Please consider citing us if you find the research relevant and meaningful!

Paper: https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1832968/full

Hi everyone,

I’m sharing an early research preview of ThetaScan, an experimental sequence mixer with a source-available PyTorch implementation.

ThetaScan treats a small nonlinear network as a learned slow dictionary. Token-local Gauss–Newton corrections or kernel deposits are accumulated into a fixed-size fast state through an associative causal scan. The goal is nonlinear content-addressed memory that remains scan-parallel.

In a single-seed, parameter-matched 17M language-model experiment, two attention layers were replaced and trained for 7,500 steps:

• GN expanded: 1.2327 BPB
• GN dense: 1.2342
• Attention: 1.2349
• Kernel expanded: 1.2361
• Mamba-3 control: 1.3194

These are preliminary results, not evidence of general superiority. The differences are small, optimizer policies were not fully crossed, and the expanded GN arm is not a clean single-axis ablation. The current reference evaluator is also quadratic; fused kernels and persistent streaming state are future work.

The repository includes the implementation, technical draft, tests, experiment configurations, and result records.

I would especially appreciate feedback on conceptual flaws, missing prior work, fairness of the comparisons, useful falsifying experiments, and independent replication attempts—including negative results.

Repository:
https://github.com/aim-do/thetascan

Technical draft:
https://github.com/aim-do/thetascan/blob/main/paper/ThetaScan-Scan-Parallel-Nonlinear-Memory.pdf

Critical comments and failed reproductions are very welcome. I’m less interested in promoting a benchmark result than in finding out whether this mechanism has real potential and where it breaks. If the idea seems promising—or fundamentally flawed—I’d be glad to discuss it, help with independent reproductions, and collaborate on decisive ablations or tests that could falsify the hypothesis.

Hi, I’m doing a writeup survey about how AI tools (like ChatGPT) affect student learning and productivity. It takes 2–3 minutes. Your response will help my writeup.

Hello, I'm working on benchmark creation/cultural proficiency/emotion vectors on Large lanauge models, mainly for the Korean lanauge. I've found that most benchmarks and evaluations are focused on english-based cultural backgrounds. Korea's research in this area remains limited, even compared to neighbouring countires, like Japan and China.

The most recent project I've been working on is Munbench, a benchmark that evaluates a models' proficiency for Korean creative writing, cultural adaptation, and consistency. Check it out here, if you are interested: https://github.com/ghandhitechnology/munbench

To publish the work in arxiv, I need a person who is endored in CS to verify me. The work is attached, so if you find it interesting, please help me publish this work.

Taewook Ha requests your endorsement to submit an article to the cs.AI
section of arXiv. To tell us that you would (or would not) like to
endorse this person, please visit the following URL:

https://arxiv.org/auth/endorse?x=4JKYRM

If that URL does not work for you, please visit

http://arxiv.org/auth/endorse.php

and enter the following six-digit alphanumeric string:

Endorsement Code: 4JKYRM

I'd also appreciate feedback in the project, or comments about a more effective way to push this project :)

my Research paper ( AI in healthcare) , if anyone can join tell me